Content Marketing & Sales Attribution In the Smart Economy

You don’t need AI to better understand the basic information about the state of your business. AI tools help extend the ability to analyze that data for hidden patterns and opportunity to improve. But simply knowing how many new contacts were engaged by sales team, how many calls and messages were shared with customers, and the current interest level among customers for each of your products is available expands management’s ability to exert control over costs and conversions.

That’s why we built these practical data points into the dashboard in the Gig Economy Group platform:

  • Number of contacts added across the network;
  • Number of distributor logins;
  • Number of distributor meetings scheduled;
  • Number of shopping carts shared;
  • Media assets viewed;
  • and more.

Sales attribution is the foundation of a well-run funnel and business. Counting up the cost and results of a sales campaign, its supporting marketing collateral and social distribution costs, to arrive at an understanding of how each dollar spent, as well as every action taken by the sales team, is the basis of a well-run business. In the world of machine learning and smart platforms, this data is the raw material used to optimize messaging and conversion, but direct-sales managers can view this numeric data themselves to see into and tune their funnel for improved results without AI assistance.

The GEG Dashboard delivers valuable human-readable metrics that sales managers and marketers can act on daily. (Sample data based on GEG app testing.)

Sales data is the meat and potatoes, machine learning is the gravy. You’ll want more gravy. We serve more meat and potatoes, too, frim the first day the system is launched.

Imagine starting the week knowing that new contacts are down by 5 percent or 10 percent or that scheduled meetings are up by 6 percent provides managers a starting point for actions to improve closing rates. Management can take action and immediately see if key activities change in the dashboard.

Real numbers

Using real-time sales activity data, a company can adjust production and inventory to lower costs in response to projected demand. This practical application of the data generated by well-defined sales processes developed at the outset of an AI project can be applied to a variety of ordinary human business tasks from Day One.

AI will improve recommendations — our initial customer data suggests overall conversion rates can be increased by 5 percent to 10 percent within weeks.

How much did a single content asset, such as a product video, was viewed by prospects? The question can be answered without AI assistance. Managers can see the results for a single asset, and how it contributed to conversion rates to the next step in the sales process, deciding whether the asset has performed to expectations. These data views support constant improvement in content development.

Where does the AI fit in? It uses the same data, examining correlations between messages, media, and engagement frequency, along with customer feedback (are they more or less interested in a product after viewing a video, for example) to propagate the messages and media that work best. Human managers will need to track the sales context in which each media asset is shared to ensure both human sales reps and the AI remain focused on the customer’s needs and personalize for the individual. Tools will help sustain a meaningful context as distributors and customers’ expectations change.

AI coaches your distributors’ creativity with personalized guidance that helps them adjust each presentation for the person sitting across the table. AI is an additional tool that coaches the distributor while management receives insights that allow better content investments.

Smart coaching

Ultimately, it is the manager’s responsibility to improve sales performance. The process begins by articulating every step of the sales process to understand which resources contribute most to closing a sale.

The data displayed in the GEG dashboard delivers more insight into the direct-selling process that can be used for daily decision-making. If, for instance, the field or an individual distributor are not making enough calls, sales managers can drill into the dashboard to understand how to coach the field for improved results. The AI features of the GEG platform help the field to personalize the messaging and presentation of data, yet it is the distributor who is the soft interface with the customer that captures the most important feedback to customer experience magic.

The combination of human insight and long-term machine-learning analysis of trends creates conversion lift. Alibaba, the Chinese online retail shopping behemoth, has found that personalization of its offers on its Singles’ Day — the equivalent of Amazon.com’s Prime Day — led to a 20 percent higher conversion rate compared to generic offers. In direct sales, management coaching can be blended with AI guidance to give the distributor better insight into the what will move the customer.

AI is ubiquitous and the most effective forms are delivering behind-the-scenes improvements that improve the customer experience. Using raw data and AI suggestions, sales and marketing teams have unprecedented access into the conversion impact of every asset and every rep. It’s a new era for sales accountability, one that starts every morning with a quick view of the state of conversion events in your sales network.

GEG surfaces important data is easy to understand and use in real-time, even before the AI gets to work. Do you have that information at your fingertips today? If not, we’d like to show you what GEG can do.

 

 

Do Not Fear Change: Customers Will Lead You To The Promised Land

Fear of change. Discomfort over breaking old habits. Reluctance to question what your company takes for granted. These barriers to change will kill a generation of companies as artificial intelligence enables salespeople who use mobile tools to deliver a personalized customer experience that connects brands to people in long-term trusted relationships.

We’ve all heard that no company can monopolize talent, so where can your organization turn for answers about sales and marketing strategy in a time of rapid and revolutionary change? Ask your customers. They know what they want. At a minimum, they know what they like and will explain how your company can satisfy their needs.

Tap into customer interest and intention with a smart content platform that reshapes sales messaging based on individual feedback. Based on early experience with our tools, analyzing and targeting content using customer feedback can make a sales rep as much as 44 percent more productive over their career with an organization.

Clayton Christensen, the author of The Innovator’s Dilemma, points out that 94 percent of corporate executives polled in 2016 were dissatisfied with their companies’ innovation performance because their marketing teams focus on identifying correlations in data instead of deepening their understanding of individual customers. By listening to customers’ feedback about their progress toward personal or professional goals, Christensen argues, the customer journey will be transformed from a trendline in data into specific related actions toward the customer’s success. The process requires interaction with customer’s needs and must reach into the circumstances in which they are making a purchase decision.

Detailed personal feedback can be applied throughout an organization to tune more than the sales pipeline, it can be used to recast the supply-chain, fulfillment processes, product designs, and the brand’s market interface, where discovery and selling take place.

The Sears gap

But fear gets in the way. Executives fall back on what has worked in the past. They resist training others to try new processes because they don’t want to change their daily management tasks. Too often, they wait until their company is mortally wounded before breaking out of established practices.

Sears, the Amazon of the horse-drawn era, stumbled along without successfully engaging its customers for decades before it went bankrupt this week. If Sears had been listening, its customers would have told the company how they wanted to interact with the “everything store” and what products they would buy. Fear, in the end, prevented Sears from turning around, even when a billionaire-savior CEO took over.

“While we have made progress,” Sears CEO Eddie Lampert told CNN,” the plan has yet to deliver the results we have desired.” What’s missing from that statement? The customer’s desire. If Sears had had a structured listening program in place that bridged its online, retail, and service centers, it may have learned what customers would buy and why. However, Sears focussed on its needs instead of its customers’ desires. That is what killed the company that connected mainstream consumers to mass-produced goods.

Ironically, Sears knew better 110 years ago. “We solicit honest criticism more than orders,” the 1908 Sears Catalog proclaimed. Why did it fail to keep listening? It did not move with its customers into new product segments while continuing largely impersonal, but inexpensive, retail practices.

Breaking out of your comfort zone

Listening is more than recording customer feedback. It requires intimate social and data science skills across many teams.  A personalized experience is lean, it is built by carving away non-relevant messages to get to exactly what the customer wants, why they want it, and what they are willing to spend. Personal feedback from customers must be applied to every interaction to eliminate unneeded information from brand messaging.

As companies build larger content libraries, they must not be tempted to flood the customer with information.  Internet-based e-commerce allowed marketers and sales teams became more focused on having plenty of collateral materials, which often stands in for personal interaction because it is comprehensive. If you can answer every customer question with a document or short video, why talk with and record the motives of a customer? With the rise of mobile technology and machine learning platforms that can process individual interaction data to help salespeople refine their message, the content library will continue to play an important part, but it will be broken down into smaller pieces and recombined to answer questions with a personal touch.

Artificial intelligence, which has been adopted by 47 percent of advertisers to target advertising content; 42 percent of respondents to the same eConsultancy survey reported using AI to generate dynamic creative content — combining different assets to make more personalized messages for customers.

A GEG action card delivers personalized media recommendations during sales calls.

Get out in front of demand

Unlike Sears, successful sales leaders tap into an almost infinite source of feedback from customers. A smart content platform uses feedback to coach a salesperson to ask more questions of customers during calls to refine the next engagement, too. Marketers are able to test content with test audiences on the fly before presenting it to all customers. A smart platform can alert sales management to changes in the expected response to each message, allowing for rapid identification of market changes. All these interactions are funneled through mobile apps, but only a salesperson can ask the spontaneous question that catalyzes buying intentions. By programming an initial set of sales steps, companies can monitor, target, and recast smaller content investments to serve more personalized interaction.

For instance, the Gig Econ0my Group (GEG) platform can track a series of messages between a sales rep and her customers, suggesting new content to share in response to a prospect’s expressed interests. GEG’s action card interface, which presents the “What’s Next” step to the salesperson after each interaction, can suggest a video or other asset to share with a customer based on: 1.) A change in interest level; 2.) An answer to a scripted question, such as “What are your goals with my product?”; 3.) Changes in messaging content and tone, which can spawn a response to an objection that includes a personalized video. These are only a few of the simple triggers an AI platform should be able to generate from an existing sales process.

Sears failed to invest in its future so many times that no business book will successfully capture its failure. (We recommend Audible CEO Don Katz’s The Big Store for a look at how, once, Sears managed to roar back.) Don’t let the fear of change prevent your company from investigating how to change for greater profitability. There’s a reason some companies survive through all ups and downs: A solid customer relationship that allows forward-looking brands to evolve with their customer’s needs.

If artificial intelligence sounds intimidating, don’t wait to find out what falling behind an AI-supported competitor feels like. A solid AI platform will take your existing sales process and optimize it. As it learns, you will learn more about how to leverage the insights generated by customer feedback. If you are afraid of the implications of AI, give us a call. We’ll do more than listen.

 

How Well-Documented Sales Processes Spur Sales Creativity

The best trapeze acts work without a net, and the results can ocassionally be disastrous when plans go wrong. Brands cannot assume their sales teams can work without a net, on pure intuition instead of using a well-documented sales process, without inviting the inevitable disastrous outcomes that result from poor planning. As the economy shifts to on-demand work arrangements, it is more important than ever to give staff a sales roadmap so that they can apply their experience within the brand experience to create even better outcomes.

New distributors need a sales roadmap, even if they plan to find their own shortcuts to success. In the fast-changing, high-churn economy in which sales organizations exist today, every step in the customer’s sales experience should be scripted in order to free salespeople to innovate smartly to deliver personalized experience. That does not mean that every action a sales rep takes should follow the script, rather the script is the grounding that gives smart salespeople the ability to improvise. Direct-selling compounds the consequences of poor planning because distributors often mix multiple jobs to earn a higher income.

“We don’t want to tell our sales reps what to do” are the most dangerous words in sales management. It’s a declaration of surrender to failure. A company without well-documented processes has abandoned its ability to learn and respond to the market. Now that content management systems can be linked to machine intelligence to track sales activity, even in individual customer interactions, a well documented sales process can improve rapidly based on customer feedback. As each distributor tries variations in language and order in which content is presented, the machine learner assesses the results and encourages or discourages that behavior based on sales results.

Change — a constant evolution of marketing messaging, sales process, and customer engagement is essential to achieving personalized experience — is the only constant in business today. Without a documented starting point, companies cannot learn how to improve from the responses customers provide to their collateral, their key sales propositions, and their marketing messages to create more personalized customer experience. The benefits of personalization are clear. It produces higher customer retention, increases conversion rates by more than 400 percent, and drives customer recommendation sharing.

A smart content platform, which blends content management and delivery with machine learning that tracks sales representatives’ experiments with different messages, can automatically test the changes that create improvements. Moreover, AI can spot messaging that fails to engage and cull content to coach salespeople to use new content that performs effectively. But none of these benefits are available without the baseline of a sales plan, against which new results must be compared.

Distributor retention grows on solid selling process

Documenting the sales process is first and foremost an investment in onboarding success. It provides talented teams the confidence to improve every step of the customer engagement. New employees consistently identify the need for clear guidance during onboarding and in daily sales activities. Without that guidance, new distributors are more likely to struggle when closing their first sales and to leave the organization before contributing to the company’s profitability.

Steven W. Martin of the University of Southern California Marshall School of Business, describes top sales professionals as firmly anchored in their company’s customer interaction strategy. It allows them to “tailor … sales pitch[es] to the customer’s needs” and creating emotional connections with the customer. Marin also points out that the structure of a customer experience allows top-performing salespeople to challenge the customer’s assumptions confidently. These intimate moments happen only in the context of a strong sales engagement, but they can be pivotal to getting a deal closed because the basis for honest feedback flows both ways.

A salesperson working within a known structure knows when to step outside the script. That is the human skill for which you are paying salespeople. When augmented by machine intelligence, a rep can quickly reshape a presentation or make a new offer based on customer feedback — data analysis delivers coaching the distributor in real-time and, as they innovate on the plan, captures the changing customer response to understand whether the improvisation by the seller is effective.

Brands that fail to inculcate their basic values and messaging strategy with distributors during the early steps in their career with the company squander shareholder resources.

If your sales leadership insists that the salesforce doesn’t need a plan, challenge them to provide data that supports their argument. You may surprise those managers by asking, and you will likely not receive a quantitative answer to the question. Intuition is untestable without a documented process against which progress can be measured.

Do not let your brand grope blindly for success without plan-based metrics that allow your team to adjust quickly to changing customer sentiment. Ensure that your marketing department has the data to act on by selecting key conversion events in the sales process, and hold everyone to the facts. Is your sales process documented to support rapid analysis of changing market and customer conditions?

 

Building Brand Trust In Gigged Markets

Trust is the currency of economic activity. Trust is the magic that humans bring to relationships. Trust is essential to the success of direct sellers and brands alike in the era of e-commerce and on-demand work. Are you building your company around the human interactions that complete the digital connection with customers who discover your business online? Is your sales team equipped with the information that will make each pitch unique to the individual customer?

Rapid changes in work and marketing have placed trust back at the center of the brand success equation after three decades of declining trust among customers. It shows that increasingly virtual businesses must support one-to-one human interaction when it is critical to the sale, retaining an existing customer, or supporting referral and social marketing activities. Human representatives are the most important interface for trusted relationships, but they must be prepared with the right information at the right time to make a compelling case for consumers’ confidence.

A history of trust in English publications

For decades, from the 1840s until the dawn of mass communication in the 1960s, “trust” was a dull topic, mentioned with declining frequency in English-language publications for decades. Using Google’s Ngram Viewer, which analyzes the incidence of words in published books, it’s simple to conclude that as the world grew closer together through physical networks, trust was of little concern as a topic of discussion. In the chart to the right, the lowest level of discussion of trust represents the highest degree of trust achieved in society since 1800.

Authors talked about “trust” less because, we surmise, it was easier than any time in history to reach out and touch someone personally. For the first time, almost every customer in the developed world was within an easy drive or a plane trip of the salesperson who closed the deal. In the 1960s, relationships were physical and deals still involved a handshake. How the times have changed in 50 years.

Trust began to collapse in the early 1970s as we became more connected to the world through virtual information, starting when microwave transmission, which enabled news networks to “go live” with breaking news, and cable television appeared.

The introduction of industrial production, cinema, and radio, along with growing networks of physical retail and company presence in communities did not shake personal trust. But when television enabled by intercontinental microwave and cable connections that allowed real-time knowledge of far-off events, people tended to find more cause to question information and to distrust reflexively.

Why trust now?

In the 1990s, when the commercial internet was rolled out, followed quickly by wireless mobile data connectivity and, ultimately, the iPhone, “trust” became an important issue that rose to the highest levels of discussion volume since the 1840s. Not since the onset of modern production has trust been more discussed in literature and non-fiction. Understanding how to build trust through digital channels using personalization and, at critical moments in the customer journey, through one-to-one human interaction, is the content marketer’s primary challenge. Each trust relationship is flavored by the brand’s message and the dynamics of the salesperson-customer interaction.

During the 1990s, a lack of broadband network capacity prevented rich media from flowing to most internet users. It was described as the “last-mile” problem. It was widely assumed that broadband would introduce an extraordinary era of one-to-one communication, that when data was flowing at broadband speeds over the last mile of cable between publisher and audience, the world would be transformed. In fact, the world was transformed, but we know now that solving the last-mile data challenge broke interpersonal trust.

Today, trust in almost all institutions is faltering. Banks, business leaders, elected officeholders, and the media all fall well below 50 percent levels, according to the Pew Research Center. Pew argued in 2017 that over the next decade, the “fate of online trust” will be decided. Brands must put themselves at the forefront of rebuilding trust because “the internet was not designed with security protections or trust problems in mind.”

Vint Cerf, a co-creator of the internet, told Pew: “We didn’t focus on how you could wreck this system intentionally.”

The keystone of renewed trust will come from the combination of people and information presented skillfully when technology cannot be humane enough to convince a customer or a citizen that the facts and promises they receive are valid. As powerful as the combination of networks and data are, the person-to-person connection, including the confidence expressed through eye-to-eye conversation and the reassurance of a handshake, must be recreated for the gig era.

As workers transition from permanent employment in lifelong careers to rapid, often daily, switching between work on behalf of multiple brands, artificial intelligence and What’s Next coaching will enable sales and service to bring deep background information to every customer conversation. They will know everything necessary to capture the objections and unstated requirements that customers share with them, and their soft skills will determine whether the facts translate into a completed transaction.

Trusted processes can be engineered into systems that act without human intervention, but actually being trusted is the ultimate human ingredient in successful sales and marketing organizations.

The tools of trust

As we’ve explored in other postings, confidence in the information provided to salespeople during their onboarding process is essential to retaining new recruits. The same principle applies to consumers, whose expectations have changed dramatically in the wake of broadband connectivity.

Today, a would-be customer may conduct hours of research before contacting a brand or filling out a form on a website. People demand information early in their product/service consideration process, and the most successful online marketers now concentrate on effective pre-sales communication to lift conversion rates.

Direct selling companies are uniquely placed to combine the reach of digital networks with the intimacy of local personal interaction. By planning a content marketing and sales process that anticipates when enhanced interaction — a meeting or a phone call, as well as video conferencing — will turn the abstract information offered in content assets into concrete promises made by one person to another. Companies’ existing content libraries are the raw material of the responsive intimate sales process described here, but it must be combined with mobile tools that help sales representatives collect additional qualitative and quantitative information through conversation with the customer.

Based on the sales process and the unique characteristics of the customer-salesperson relationship, content can be reshaped on the fly to address customer concerns, as well as coach the sales rep to ask for the business at the right time. Using this roadmap, even negative results can be integrated into the sales process to refine the message and improve conversion rates. Artificial intelligence, such as Gig Economy Group’s machine learning techniques, can spot effective or ineffective messages long before human managers would discover trends in quarterly or annual sales reports.

Trust-building interactions are the engine of improved efficiency for business. Bringing the entire company’s resources in the form of data and contextually relevant content to each customer interaction provides feedback to improve products, reposition resources, and evolve messaging. It all begins with trust, but every transaction ultimately leads to a human connection or trust begins to falter. Have you prepared your sales and customer service teams to be magically aware of customer concerns, ready to send the right message at the right moment with a personal touch?

How AI-enabled content is different from traditional content management

We are often asked what is the difference between an AI-enabled content platform and the content management systems used by marketers today.

Briefly, artificial intelligence (AI) adds the ability to learn and adjust content programs based on the success or failure of a change to influence conversion rates. Traditional content management systems (CMS) can be scripted to perform feats of personalization but lack the capability to learn from changes. In the one-to-one sales setting of the home or direct-selling meeting, AI can track any changes in representatives’ sales messages and understand if they help improve revenue.

That’s a pretty dense and, we believe, concise explanation. Here is what it means to your organization:

  • Traditional CMS systems provide effective scripted customer experience, but they cannot learn and improve without human intervention;
  • AI-enabled content platforms can learn and even test changes to customer experience without human intervention;
  • The in-home and one-to-one selling environment requires rapid testing and dissemination of novel messaging that converts to desired actions at each step in the customer journey;
  • AI-enabled content platforms give distributors active coaching that captures customer input and improves sales messaging by personalizing each interaction, and;
  • AI-enabled content platforms reduce management overhead by automatically testing and reporting results to sales and marketing leadership, who can make better-informed decisions about the brand message based on more customer feedback than a CMS can collect.

Content marketing does an excellent job of delivering programmatic content, but it fails to understand the changing context of the selling relationship. As selling moves from retail to online, as well as into intimate contact with the customer in their home using mobile services, contextual changes in sales content will be the key to satisfying personalized experience.

Fixed versus Evolving Content

Now, let’s dig into the details of the different approaches to optimization of marketing and sales messaging made possible by AI. The advantage with AI is simply this: It can measure everything going on in the funnel rather than just those actions your team chooses to experiment with and track.

Traditional CMS systems have achieved high levels of personalization based on extensive scripting that uses conditions, such as the customer’s most recent action or demographic data, to direct them down a pre-fabricated sales path. The customer experience can often feel rigid since the workflow can be changed only by a content manager. Decisions to try a new word in a campaign or a novel order of message delivery, for example, are driven from the top down and involve A/B Testing and other methods of measuring changes in business outcomes.

Gig Economy Group’s AI-enabled content platform watches all the actions of all the sellers in the field. Our action-card interface suggests messaging text, allowing the seller to change the email text they use to, for example, share a media asset with a customer. Each of these changes is an experiment at the edge of the network based on the seller’s insight into customer responses to earlier steps in the funnel. They would be impossible in the fixed-content structure of scripted content workflows that don’t allow unanticipated deviation at any step in the customer engagement.

From the traditional CMS perspective, changes made in the field to selling materials and order of delivery are unexpected and consequently unmeasurable. A machine learning service may be able to assess responses from customers using natural language processing, however, the CMS will simply report the new condition to a human user, who must decide whether it is significant and worthy of an investment in testing changes to the content delivery scripts.

From Content Marketing to Contextual Selling

Machine learning, the form of AI used by Gig Economy Group, can ingest any changes and, by tracking changes in known conversion events in the sales process, determine whether a reps’ use of a new salutation in their email communications, such “Hey, Friend!” or “I’ve got a secret to share with you,” translated into improved conversion.

AI-enabled content listens and responds to the rep, acting like a coach to help them present the best story that sells possible. If a unique twist on the selling process is successful with one distributor over a dozen interactions, the AI will test the change in other distributors’ suggested messaging, literally inserting the new language or re-ordering the presentation of media to determine whether it will work generally. These small experiments quickly prove or disprove the value of many changes while constantly refining the brand sales experience.

Sales representatives should be able to adjust every element of their communication to address the person they know more intimately than the platform suggesting messages. This provides bottom-up and widely distributed experimentation that can surface not just better next steps, but also the potential for a new market segmentation strategy. For instance, if in selling a business opportunity a distributor finds that her business-interested contacts consistently want to try the product before enrolling, she can start a trial purchase workflow that the AI recognizes and tracks as a new path to a known conversion event. The result is many sales process improvements with less management overhead required.

With well-defined sales processes established during onboarding to an AI-enabled content platform, the tools will surface productive changes in individual representatives’ workflows, as well as signal to management when a rogue distributor is failing to generate sales because they’ve deviated too far from the brand message.

Are you learning everything your market is telling you? If your CMS is not able to understand new sales paths, you will be blind to the improvements that customers and representatives invent. And in a resource-constrained market, those lessons are the hardest to embrace if your content platform isn’t looking for unanticipated improvement.

If you’d like to learn more, sign up for a demo of the GEG platform now!

Reinvent Onboarding With Personalization

Getting started in direct selling is exciting. The new enrollee is on the receiving end of intense focus by sponsors and the company, but that quickly passes for practical and understandable business reasons. Then, the new distributor too often is left on their own with no roadmap for success. Personalized mobile apps can step in to extend the one-on-one onboarding experience.

During the initial days in a network, a sponsor may talk with the enrollee daily. Learning must deliver the enrollee a sense of purpose to ground new distributors in company mission, policy, and how they will be measured for success. In those first days, there are many policies to remember and sales actions to start growing the funnel of prospects. But within days, sponsors are on to their next recruit and often leave an enrollee to fend for themselves, long before the Society of Human Resources Management’s recommended minimum of 90 days of onboarding.

With an app in hand, enrollees can dig into a library of company learning, be prompted to start sales activities, and receive feedback and recognition based on their success. These activities can be monitored and reported to sponsors or management, allowing them to reach out with encouragement and individualized next-step guidance with a single tap.

Reinvent your onboarding experience by taking the time to associate content and sales process steps to measurable events in a smart sales platform.

A smart sales content platform can shepherd new distributors and provide reinforcement of learning and company best practice with automated recognition messaging. Sponsors can also receive updates about their enrollee’s activities and reach out when it will make the biggest difference, saving them time while accelerating time-to-first sale by their newest team members.

Assembling An Onboarding Library

When breaking down existing training content for ingestion into a sales automation platform, a crucial step in a company’s adoption of smart tools, content should be linked to identified next actions the new distributor can take to become successful using brand content and processes. Early users of the Gig Economy Group-based LifeVantage App describe the resulting experience as “like having a personal secretary reminding you to follow-up,” according to Jacqueline, a reviewer on the Apple App Store.

Begin by separating basic company knowledge, product introductions and product knowledge sequences, as well as initial sales actions and related sales skills videos. Address each group of content assets separately, always thinking about where in their onboarding the enrollee will engage with each type of content.

“Great job” is the most potent phrase for extending onboarding, and your company can send that message whenever a recent enrollee takes an important step. Action cards, such as the content sharing recommendation to the left, can be triggered based on distributor actions, prospect behavior, or company policy, giving guidance and encouragement.

Action cards provide suggested content and messaging to distributors, which they are free to accept, reject, or modify. GEG’s platform tracks and learns what works best.

Gig Economy Group’s action card interface, for example, can be configured to respond to activity with recognition messages (“Attaboy!”), related information, or the what’s next activity necessary to progress. Recognition, in particular, should be tied to:

  • Viewing all of a video or an entire sequence of videos;
  • Adding a contact;
  • Sharing content or sending a message to a prospect;
  • Following up with a prospect after a sales step, such as sharing media or a shopping cart, and, of course;
  • Converting a sale or getting a prospect to increase their product interest.

The same triggers can be set to send the enrollee’s sponsor or a sales team member a message alerting them to how the onboarding is going.

As your automated onboarding evolves, take the time to return to the management interface to add new tracked events, such as a distributor’s lack of activity, difficulty moving a prospect along the funnel (signaled by repeated sharing without any change in prospect interest level), or positive results to spur the sponsor to communicate.

Reinforcing action, which more than 80 percent of new distributors fail to take, can dramatically improve early sales success. It’s also important to allow new distributors to decide for themselves.

“I love that you can delete suggestions [in action cards] since they aren’t always the right choice for a particular person,” wrote one Apple App Store reviewer, JudiPP, of the LifeVantage app.

When onboarding, and throughout the distributor’s relationship with a company, people want to know what to do next, not take arbitrary orders. Allowing people to experiment with their selling style is essential to their sense of efficacy, and the variations they introduce into the process is fodder for the smart platform to learn.

Sales and marketing leaders can use existing content and new data-targetted content production to create a genuinely inviting onboarding experience that creates a conversation between enrollees and their sponsors long after traditional welcome activities end. Well placed triggers in the onboarding and daily sales process can alert management and sponsors to distributors in need of help, or just a push toward activity.

 

How Salespeople Can Start Selling On Day One

Helping a new distributor during the “golden two weeks,” when those enrollees who close their first sales or distributor enrollments are most likely to become a high-earning, long-term member of a direct selling network, is the best onboarding investment. Bar none. It moves the potential sales rep toward confidently repeating the company’s sales process. Getting new enrollees to “work the system” from Day One with an organization creates the bond that drives network growth and improved revenue.

Distributors who start sales activities and close sales within 10 business days of enrollment will earn 71 percent more than a peer who takes just two weeks longer to make their first commission, an analysis of nine years of sales data by LifeVantage found. Direct selling trainer ServiceQuest reports that a 10 percent increase in distributor retention will produce 49 percent more revenue over 10 years compared to unengaged distributors.

The Gig Economy Group (GEG) platform and app eliminate all tool-centric training, providing easily understood functions to do one action at a time.

Machine learning tools can coach a newbie from their first moments with a direct selling company, but the most important action automation can facilitate is the adding of new prospects, initial messaging to those prospects, an established pattern for follow-ups and content sharing to build the prospect’s confidence in the salesperson, the company, and the trust relationship that will result in ongoing sales and auto-ship registrations.

McKinsey concluded that sales and marketing uses of Artificial Intelligence — the catch-all description that includes machine learning — will produce $1.4 Trillion to $2.6 Trillion in improved sales and marketing performance, with more than two-thirds of the value coming from enhancement of existing analytics. Your sales process, if mapped as part of machine learning adoption, is the raw material needed to increase revenue and retention.

The problem, or rather the reality is that 80 percent of new distributors never take any action. They either fail to take any action or get bogged down in trying to understand the company and the products or services they’ve signed up to sell. Without sales actions, there is no data to use when optimizing sales procedures.

First and foremost, direct selling companies must get new enrollees to start adding and working prospect relationships.

What’s Next is Step One

Focus new distributors on two necessary goals on their first day: 1.) Understanding their new company’s values, and; 2.) Adding and reaching out to their first prospects.

We’ve discussed how to map your onboarding process here. Let’s concentrate on the problem of getting people to act. Throughout any guided experience, whether it is delivering sales coaching or interpreting marketing data to suggest better selling steps, the “What’s Next” approach to app user experience is the most effective means of getting people to move through a sequence of activities to achieve a goal. During the first two weeks with a company, new distributors remain unsure about the company and its mission or processes.

A LifeVantage App action card suggests a video to share with a new prospect based on their interest, and over the next two days will remind the distributor to follow-up, along with the appropriate content so share next.

Onboarding content that provides a clear, concise narrative about the values and mission of the company sets the stage for action. Then, the barrier becomes the complexity of the tools themselves.

Too often, apps require users to learn many tool skills and go about it by walking through many steps before allowing people to start using the tool for its primary purpose, such as adding and communicating with a new prospect. As apps grow more sophisticated, these learning processes become more complex, raising barriers to success for the distributor who needs to do simple steps in the simplest way possible. Consider the vast breadth of capabilities of Microsoft Word or Adobe Photoshop, which most users never need and will not explore without a specific context, getting their job done.

Artificial Intelligence apps have to stay focused on the human actions they support, hiding all complexity that will prevent an aspiring distributor from taking the actions necessary to close their first sale. The Gig Economy Group (GEG) platform and app eliminate all tool-centric training, providing easily understood functions to do one action at a time. For example, on their first day, a distributor is asked to enter one or more new contacts. There are no elaborate instructions, just an “action card” that suggests what to do and, with a tap of a button, the tool to do it in the simplest form possible.

But data entry is not the salesperson’s main interest or a reason to be enthusiastic about their first day on the job. The GEG platform ingests the new contact data, reviews the information, and immediately suggests recommended messaging and content to share in order to start the prospect conversation. After the distributor sends their first outreach message to a prospect, the platform monitors whether the content has been viewed, as well as any responses sent by the prospect, so that it can coach the new enrollee toward the sale.

For example, in the GEG-based LifeVantage App, the action card (see image to the right) is generated in response to a new contact entered in the app. Assessing the prospect interests entered (or not entered) by the distributor, the platform suggests a specific video program to share with the contact to begin the conversation. If the distributor accepts the recommended action, the app delivers suggested text to use when sharing the video in the next screen, which is part of the messaging toolset. But the distributor’s experience remains focused on their next step in the relationship rather than navigating between different tools.

In this case, AI smooths the technological overhead of a complex set of application capabilities, leaving sellers to emphasize their strengths, which are developing relationships, choosing the right words, and delivering the information a customer needs at exactly the right time. At the end of Day One, the distributor has seen three short onboarding videos and has at least one, if not the recommended five, prospects in motion. Those actions translate into commissions, which keep distributors engaged and eager to grow their business.

Selling is hard work. Make it easier for new enrollees to concentrate on their strengths instead of the tools they must use to grow their personal funnel and move prospects toward the close. What’s next should always be related to the state of the distributor-prospect relationship, not the distributor’s competence with a set of digital tools.

Mapping The Sales Journey: Machine Optimizing Customer Experience

In the previous posting, we explained how to break down a content in an existing digital asset library into major categories, Onboarding, Prospect Development, Product Knowledge Development, and Sales Skills Improvement programming. Each of these phases of the distributor and customer journey must be inventoried and the expected outcomes to be produced by each asset identified.

As a team begins to use machine learning, the next step is to focus on the customer journey in its Prospect Development content, because it has the greatest influence on conversion rates and revenue. These assets provide a machine learner with measurable steps in a customer journey and the team’s job is selecting what to measure at each customer touchpoint.

Focus on the process of moving a prospect from initial awareness into a distributor relationship or to the close of a product sale. Set aside the social content used to attract awareness along with the content used to train and inform distributors right now. The video, articles, product sheets, and other materials shared between a distributor and a prospect are the only concern at this step. Don’t be afraid to throw out content that doesn’t fit and to plan the production of new content that may work better.

Consider each content asset as though it is a candidate when hiring a sales support person to work with distributors to successfully complete the Prospect Development sequence. Is it up to the job? Can it be described completely so that its “boss,” the machine learner can understand what it is expected to do?

“Just as you wouldn’t hire a human employee without an understanding of how he or she would fit into your organization, you need to think clearly about how an artificial intelligence application will drive actual business results,” wrote Greg Satell, author of Mapping Innovation: A Playbook for Navigating a Disruptive Age, in the Harvard Business Review this month. The metrics identified at this step in the machine learning onboarding process are the equivalent of a job description for the machine learning platform.

A machine learner will analyze the performance of content assets, the sequence in which they are presented, and the messaging that drives views of the content, comparing the results to the expectations and metrics identified during this exercise by sales and marketing leadership. When the system identifies departures from those expectations, the system will seek alternative routes to improved conversion by testing different combinations of content and sales messaging. It sends suggested next steps to the distributor, which they can use or modify (creating more variations the machine learner can analyze), measuring all the results against the goal of speeding prospects to the close.

What does the machine learner need to know about each asset? 

What is the asset about? What is the subject, as well as the keywords, themes, and who or what appears on-screen? Metadata is often missing and may be added in the content management platform. If an asset does not have extensive metadata describing its content, that must be created so that the machine learner is able to test different combinations of assets. For example, if the machine learner knows that a video features a female presenter, it could test that asset with female viewers to see if it converts better. There are myriad combinations of demographic and psychographic factors that can be tested, but only if the asset is thoroughly described in a way the platform can understand and use.

Where is the asset positioned in the current selling sequence? A machine learner may also test different sequences of content to understand if existing content can produce better conversion rates.

Is it the traditional first video shared with a prospect to create interest? Does it depend on any other assets for context, such as a previous video in the sequence? This information is important to preventing the machine learner from rearranging content in a way that doesn’t make sense to the recipient. For example, if your company uses jargon frequently, such as referring to a product using an acronym (e.g., “Comprehensive Weight Magagement is spoken about as “CWM”) it should be explained before it is used in other contexts. Telling the machine learner that one asset must precede another prevents customer confusion because information is presented in the wrong order.

What is the expected outcome of the customer’s engagement with an asset? Is a video or a sales action, like making a call or presenting products at a meeting, expected to increase customer interest? Is there a specific call to action associated with an asset, such as a link to send a message to the distributor who shared it? Is the expected next step after a distributor makes a presentation a purchase, a call being scheduled, or a specific follow-up asset should be shared and viewed? Documenting these expectations provides the machine learner with extensive options to test in different sequences. As long as each expectation is documented, your organization has the basis for a measurement of the response.

What is the expected pace of a complete sales motion? If there is a six-step sequence associated with selling a health product today, for example, are the assets performing satisfactorily as a unit? Is the distributor taking too long to present the steps? Are prospects responding in the expected timeframe? These pace-related signals catalyze machine-generated coaching for the distributor, reminding them to follow-up in the optimal timeframe to make a sale.

In the Gig Economy Group platform, clients can configure specific follow-up questions for the distributor to ask the prospect so that qualitative and quantitative feedback can be captured by humans. Determining whether the prospect more or less interested after seeing an asset or participating in a meeting often requires the sales rep to interpret statements and signals. This ability to interpret the impact of an asset is the distinct advantage in-person sales provide to marketers. Leverage it by developing follow-up questions that can be turned into metrics.

Attribution can be controversial. It is a mistake to lump social and other content together with your sales assets because social content often has different goals. However, as a direct selling company captures more information about its distributors and market, the opportunity to use assets in a different context, for example by adding a personal success story normally shared in social channels to a sales sequence, will emerge. The outcome is a more productive asset library with more applications, which can increase the ROI on every content investment.

With this sales process inventory in place, distributors can be equipped with an evolving selling process that can deliver ongoing improvement in revenue with greater distributor confidence and retention.

Onboarding to Machine Learning: Mapping Sales Processes

Improving a sales process with machine learning starts with a straightforward assessment of the existing content, including video, audio, text, graphics, and training, a company uses to onboard a new distributor to its policies and practices. These first steps, which set the stage for confident selling by new distributors, are essential to improving sales success during the first two weeks with a new direct selling company. People who close their first sales within 14 days earn an average of 71 percent more than a distributor who takes just four weeks to complete a sale.

Sales and marketing leadership tackling machine learning for the first time need to break their existing onboarding practices and initial selling activities into steps, then organize those steps into collections that are expected to produce a specific result that can be measured. We recommend assembling a map of the onboarding, training, and sales support experience for new distributors, as their immediate success will produce immediate improvement in revenue and profitability results. Tier your product content in terms of 1.) Company overview and welcome programs and content; 2.) Selling materials and programming for distributor use; 3.) Deep product information, such as sales sheets or detailed product knowledge videos.

Break down the first month of distributor experience into:

  • Onboarding: Introduction to the company, its mission, and selling process at the overview level — what you most want your new enrollees to know on Day One and to have internalized by the end of Week One.
  • Prospect Development: This is the first, most important step for a successful sales enablement tool. Rather than explain how to use the contact management tools, get the distributor to work immediately on adding prospects and following up.
  • Product Knowledge Development: Ongoing and frequently updated, product knowledge and product-specific training.
  • Sales Skills Improvement: If there is sales training content that is not product-specific, such as coaching on how to follow up or present at a meeting, these programs will be useful throughout the entire distributor lifetime, not just as they become familiar with the company.

We suggest beginning with a list of all existing content. Write the title of each asset on a sticky note and, on a second note, the goal for the asset, such as “Create a sense of welcoming support” or “Establish product- and lifestyle-claims policy.” Place the two sticky notes, asset and goal for the asset side by side. Examine all the content related to onboarding to see if there are multiple assets seeking to achieve the same outcome.

As common goals are identified, cluster the content assets by the expected outcome. It is likely there will be several assets that drive to the same distributor goal, and these variations are natural places for a machine learning content system to start testing to see which content assets are most effective.

Introductory content, such as a generic welcome message and overviews of the company, should be separated from practical how-to content related to using tools and services offered by the company to refine distributor sales skills. The latter training content will distract distributors from mission-centric learning. For example, most direct selling systems begin with a series of introductory videos about the company, its products, and how the distributor can start to work its selling process. These videos set the stage for future training, but they have a narrow set of goals: To build confidence in the distributor that they’ve made the right choice of product or service to sell, that the company is reliable and supportive of their success. This is essential for winning younger distributors’ loyalty.

With mission- and policy-centric content organized into the first category, the next step is to organize each of your sales task workflows for use by the machine learning platform.

Each days’ distributor training activities during the first two weeks must have a goal, such as confirmation that the new distributor understands the basic value proposition and mission of the company or that they enter and start communicating with prospects. And each day’s activities should contribute to the next day’s goals — if on Day One, the distributor enters five contacts, Day Two should include follow-up activities and content that help convert those leads to a call, presentation, or online meeting.

Look for multi-day processes, such as prospect development and determine whether multiple assets address the same steps and issues. These are convenient reference points when thinking about how to shorten and improve onboarding programming, which can produce immediate improvements in distributor success. Sales process steps in a “What’s Next” machine learning tool allow the distributor to focus on doing sales work instead of learning how to use tools.

Once the Welcome and Onboarding workflows are complete and redundant content identified for testing, the organization of product knowledge and sales skills coaching content if there is any in the current asset library. These are content categories that can be populated over time, as well as licensed from training providers for integration with sales coaching machine learners, which can target sales training based on the distributor’s sales challenges. For instance, if they consistently add contacts, get meetings, but don’t close, the tool can direct the distributor to training videos about closing, getting commitments, and handling objections.

With a smart platform in place, a variety of training programs can be added to address your network’s training needs and to address individual distributor challenges. In the next installment, we’ll explore attribution modeling for machine optimization of each step in the sales process.

LifeVantage: Machine Learning In Direct Selling

Seeing is believing for Sandy, Utah-based LifeVantage. CEO Darren Jensen presented the sales results of LifeVantage’s early implementation of machine learning at the 2018 Direct Selling Association Conference in San Diego, reporting that distributor retention is up 34 percent overall in its 2018 fiscal year, which ends this month. The reason is improved visibility into the state of the business with the ability to intervene with new content and messaging to the individual distributor.

“We can now see if people are getting stuck at any of the [steps in the sales journey],” Jensen said during the presentation. The company, a leading seller of nutriceuticals and beauty products, is Gig Economy Group’s first commercial customer. Although its machine learning tools have been available only for a few months, LifeVantage’s pre-launch analysis of the sales process resulted in rapid improvements in novice distributors’ time-to-first-sale and, by extension, retention rates and average revenue per distributor.

Instead of looking at the whole process “once a year” based on annual sales results, Jensen said LifeVantage now relies on leading indicators, such as the number of contacts being added by distributors as well as meetings and calls presented by distributors. “Now we can see deep into our funnel,” he added.

LifeVantage CEO Darren Jensen speaks at DSA 2018.

Planning for machine learning in its business brought LifeVantage management face-to-face with each step of its sales process, raising new questions about how to achieve the highest revenue and revising the company’s basic assumptions about where to invest. LifeVantage’s comprehensive review of its sales methods and marketing content has recast management activity to focus on tactical changes to messaging and selling process that have delivered continued improving results.

“GEG sat down with us to devise systems and technology to answer and resolve the sales issues we have,” Jensen said. The process, which involved quantitative analysis of almost a decade’s worth of sales data, revealed three key principles that govern decision-making:

  1. Accelerating the first dollar earned by a distributor is the most effective investment LifeVantage can make in retention.
  2. It is equally valuable to sign a new customer or distributor. Because 66 percent of distributors begin as customers, LifeVantage deemphasized the traditional focus on having new enrollees recruit new distributors. LifeVantage also found that customers stayed longer and spent more money than unengaged distributors.
  3. The speed to the first sale by a new distributor is critical to their long-term success. LifeVantage found that if a distributor makes their first sale within two weeks of enrollment, after a year they earn an average of 71 percent more than someone who takes just another two weeks longer to close a sale.

Taking the next step

Direct selling is poised to evolve, adopting greater transparency and digital tools to treat distributors as key partners in success, according to Jensen. As retail and e-commerce companies, notably Amazon, press to gain access to the home, direct sellers enjoy a unique, temporary opportunity to take a greater share of U.S. and global consumer revenue. Shaping each customer experience to address personal concerns and values is mission critical.

The addition of machine learning lets the company “deploy technology to be sure people are closing in the right way to establish a trusted relationship,” Jensen said.

An Action Card: The distributor has just shared a product video with a prospect and will be reminded to follow-up when the video is played.

For example, LifeVantage now focuses intense effort on getting a new distributor to close their first sale. Simply winning their first dollar in revenue increases distributor retention by 44 percent over the lifetime of the enrollee (see LIfeVantage image above, which shows the likelihood of a distributor placing a monthly order based on how much they earn cumulatively). To accomplish this, LifeVantage provides each distributor a free machine learning-enabled app that begins training and sales activity on their first day with the company.

The app, which runs on the Gig Economy Group platform, reminds distributors what they’ve shared with prospects and how to follow up through a customized set of “action cards” delivered to each distributor. Action cards can display training content, product knowledge programming, sales guidance, and relationship management tools so that the distributor is always ready to do What’s Next to succeed.

“The first network marketing company I signed up for was one of the most exciting things I’ve ever been part of,” Gig Economy Group Senior Vice President of Business Development Yak Gertmenian, who spoke with Jensen on-stage. “Two weeks later it started to wane because I couldn’t find anyone to help me. I got stuck in the What’s Next trap. I didn’t know what to do.”

In addition to training, action cards provide suggested content to share with prospects, recommended messaging ideas, and follow-up reminders. These cards sent by the GEG platform to each distributor based on their sales skills, communication habits, and, importantly, customized messaging flows for engaging each prospect based on their expressed interests and feedback from the distributor.

Existential questions

Direct selling now competes for distributors with many more options for side income, a challenge LifeVantage sees as life-or-death.

“The next economy is here,” Jensen said. “We are at a tipping point where [direct selling] can become a leading industry or become irrelevant.” He recounted a seeing a recent Facebook add for Shopify, the online commerce tool, that claimed to provide “the best side hustle” to make extra income. As the gig economy evolves, 30 percent of Americans have embraced added income sources, ranging from Uber to selling.

“We are competing for the “side hustler” with multiple industries,” Jensen said. He told his direct selling colleagues: “We need to compete with all these companies at a higher level.”

By embracing machine learning, LifeVantage has learned to customize the onboarding and training experience, helping to increase success when it matters most. The results have been rewarding. In 2015, only 26 percent of new LifeVantage distributors completed a sale during their first two weeks with the company. By 2018, sales in the first two weeks after enrollment reached 36 percent, a 38 percent increase overall.

Interestingly, the sales lift extends past two weeks, even though attrition soars after the first month. LifeVantage also reports that sales in the first month after enrollment has increased from 55 percent in 2015 to 67 percent.

Working from well-aligned principles, LifeVantage and GEG developed a sales process that has allowed LifeVantage to weather the dismissal of a third of the company’s sales force due to unauthorized overseas sales without a decline in revenue.

“Technology can you extremely resilient as well as position you for greater success in the future,” Jensen concluded. As machine learning runs daily, LifeVantage’s insight into their funnel is propelling ongoing content and messaging changes to improve conversion success.

In our next installment, we’ll explore how Gig Economy Group translates client business processes into measurable sales workflows ready for machine optimization.