Posts

Mapping The Sales Journey: Machine Optimizing Customer Experience

/in /by

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.

Getting Started With Machine Learning for Sales Content

/in /by

Human insight and inspiration are the basis of solidly profitable applications of machine learning to sales processes. LifeVantage, which recently launched a Gig Economy Group-based sales app for its distributors, improved its sales and marketing alignment as part of their onboarding to machine learning.

The GEG process builds on five principles that ensure clients onboard to improved, personalized sales experiences:

  1. Attribution analysis provides useful metrics for assessing content and messaging.
  2. Design to deliver “What’s Next” automatically, offering distributors the choice to use or refuse the machine suggestion.
  3. Make the process repeatable and scalable while supporting testable content and messaging variations.
  4. Deliver real-time leading indicators to management to facilitate their own content production decisions instead of blindly following machine recommendations.
  5. Provide a proactive view of the business from Day One.

We began the LifeVantage process using the company’s existing video, audio, and PDF-based content assets. Those assets served as the foundation of a machine learning platform. Assigning expected outcomes for each of these assets, whether LifeVantage management believed the asset moves a prospect toward purchasing, provided the GEG analytics system to identify opportunities for optimization. Each surprising outcome lets the machine learner choose alternative steps and test their efficacy in the funnel.

The teams assessed content provided to distributors as they join the organization, and consumer marketing content. Every aspect of the interactions between LifeVantage sellers and customers were examined and cataloged. With this inventory and a list of expected outcomes at each step, a machine learner can look at every interaction to identify what content works best, which messaging spurs prospect action, and the optimal order for delivering marketing content to customers.

Having innovated early with several technology vendors to develop four mobile apps, LifeVantage found its new distributor onboarding had become fragmented across several applications. Each app addressed different aspects of learning the LifeVantage Way, the selling process, and ongoing training. The company realized its distributors needed a single point of contact with LifeVantage information and its backend sales management systems. Simply providing training through streaming videos, audio programs, and coaching by sponsors would not be sufficient to keep a new distributor engaged if their early investment of time in LifeVantage did not convert to sales.

Results, Before The Machine Kicks In

The LifeVantage app for iPhone and Android connects distributors to the company’s media catalog and sales platform to provide next-step guidance for each phase of the selling process. Building on its established training and invaluable guidance from its most successful distributors, LifeVantage analyzed each step in its selling motion to create a machine learner capable of recognizing the salesperson’s progress in training, achieving product competence, and how each relationship they are working is progressing against network-wide benchmarks.

It was clear from the start a mobile-first strategy was essential. In many cases, distributors used paper-based sales management tools rather than PC applications. iPhone and Android versions of the app became priorities for the team, who moved to the design stage with a challenge: How to make the seller’s success as simple and pleasing as an Uber rider or driver’s experience.

A significant finding in early surveying and interviews centered on the design on framing seller’s choices. Distributors did not want to be told they have only one option, which they must follow the instructions given precisely. Instead, they sometimes want to skip sharing a video or time the gathering of feedback differently than established LifeVantage practice. For newcomers to LifeVantage sales, spontaneity in communication reinforced their sense of confidence. The team had to allow users plenty of flexibility. Experienced distributors moving from other network marketing companies were less inclined to watch training, but eager to get to work.

Through internal discussion and continuing interviews with experienced distributors, the team settled on critical metrics to change with the app that focused on novice sellers. While the first version of the LifeVantage app does offer services for long-term distributors, the newest seller is the next source of growth at the company. The selling steps and events that lead to initial success, such as time to first contact added and messaged, the frequency and success of the new distributor’s meetings, and speedy progress toward the first sale and progress up the compensation ladder became central to the project.

Customization At the Design Stage

Rapid development demanded continuous collaboration between the design and development teams during the onboarding and launch process. The initial distributor interviews produced a design the team decided required too much tool knowledge on the user’s part. The needed to know steps when adding a contact or creating a meeting, for instance, gets in the way of completing the task. A key decision resulted: Each action card that called for a distributor task should open the workspace where the work takes will be performed and, on completion, acknowledge the progress made. Sellers want feedback from their app about their progress.

App performance, too, presented challenges. Machine learning is an evolving computationally intensive technology. Building in a robust development environment on proven cloud systems was essential to fast responses to user input in the LifeVantage app. Feedback from distributors came fast and frequently, providing many signals about where to prioritize early investments.

The internal knowledge that had seemed concrete turned out to be merely intuitive guesswork in some cases. App usage demonstrated that new distributors wanted to get to work by starting to communicate instead of going through extensive training. Consequently, training sequences were shortened and selling steps moved to the “top of the deck” of the new user’s experience. Early usage showed distributors gravitate to creating new business. Consequently, LifeVantage recast much of its extensive media catalog as daily training material presented contextually while the distributor is performing a related task. When related to an immediate sales challenge, such as gaining commitments, LifeVantage’s training content proved even more effective.

Designers and developers worked closely to move the beta through seven release cycles, each shared with the beta community. Additionally, 20 top LifeVantage distributors and a cadre of 20 newly registered distributors were engaged to give weekly feedback. The result was hundreds of changes to the functionality and design captured over three months that would have taken a year to collect through traditional channels.

The first general release of the LifeVantage app delivered a simple-to-use tool for learning, selling, and supporting customer and distributor network relationships. It integrates the LifeVantage’s Media Library, Contact Management, Meeting and Feedback Management, as well as providing business performance information that assists sponsors in training their distributor networks. From first contact and entry of personal data into the app, through media sharing, it prompts distributors to make calls and send messages that capture prospect feedback. Based on what the prospect does, the app selects from a variety of optional next steps, such as triggering a distributor enrollment or shared cart with a potential buyer.

What’s Next Is Personalization

/in , /by

Personalization of customer experience requires two investments: machine learning-based targeting technology, and; human intelligence to interpret customer moods and feedback, as well as curate the media and messages served up for sales use. One without the other will create an inhuman and untrustworthy customer experience that feels to both worker and customer like following rules instead of exercising their passion and fulfilling their needs.

No one wants to be automated, subjected to rigid rules that cannot change, but anyone dealing with lots of information can appreciate being helped by automation. The choice to be assisted by your company’s automation is the critical offer every future employee, contractor, and a customer will consider. The future will be decided outside your organization, by those who can make it a success. These are the personalization-readiness questions to answer in 2018:

  • Is your company’s sales team ready to change based on measurable feedback?
  • Are sales and marketing teams equipped with content and tools, including mobile apps and social network integrations, that help capture feedback that crafts a personalized experience?
  • Is your company organized around constant progress towards distributor and customer personalization?

“Digital technology makes the customer the star,” according to ZEITGUIDE, an influential trend-watcher in New York, and while stars need technology it is the audience they need most to achieve stardom. The What’s Next model, which serves the right content to a salesperson at the appropriate moment to close or move a sale forward, can be extended to provide unparalleled post-sales engagement.

Human interaction is the basis for turning each customer into a star influencer on behalf of the brand – these person-to-person interactions are where creativity and variations on machine rules invented by a human create the surprising experiences that customers remember and share.

Here is the essential shift of context necessary to achieve personalization with limited resources: Think of your marketing, sales, and support teams as the customer’s audience. Companies have tended to think of the relationship the other way, treating the customer as the audience. The brand’s job is to deeply understand the customer, reflect the customer’s desires through the organization, and deliver the fulfillment of those needs as “star treatment.”

Think of your marketing, sales, and support teams as the customer’s audience. The brand’s job is to deeply understand the customer, reflect the customer’s desires through the organization, and deliver the fulfillment of those needs as “star treatment.”

The star experience is based on a series of actions, literally what the expected next step in the marketing and sales funnel, laid out by company leadership and tested through interaction with distributors and customers. A rigid and unresponsive customer experience will always fail because every customer and all the sales and marketing people who interact with them brings different criteria for success. Every star is unique and wants to be treated as their own end, not simply the means to revenue.

The star treatment is a form of mass customization. Applying available content to telling a personalized story based on targeting factors. The next step in the evolution of on-demand markets will require breaking down content, processes, and the measurement of success into micro-steps that can be personalized more efficiently.

Rise of the Augmented Worker

The rise of the machine intelligence is widely seen as a threat to human employment. We see a new challenge for human workers, an increased focus on service and care, which will extend far beyond familiar caregiver roles, such as assisted-living for seniors and physicians’ assistants using AI to replace doctors in many clinics. Doctors are now freed up to spend more time with emergent and chronic care patients – they are not disappearing, just moving to a different level of caregiving.

The next generation of care-delivery roles will be the interface between highly efficient supply chains and customers. Market research firm IDC projects that the combination of customer data and artificial intelligence will create 471,819 new jobs this year, as people augmented by machine learning fan out to improve customer experience in novels niches, adding $1.1 Trillion in new revenue top the economy by 2021.

The business of caring will include marketing, which must understand and anticipate customers’ needs during pre-sales engagements, sales staff that modulate the delivery of marketing content and personal messages to the customer, and a wide range of post-sales services. For example, many direct-selling distributors provide personal training services along with the products they sell. Markets are fusing products, services, and human functions into a continuous customer experience in which the salespeople play essential supporting roles for the organization and customer.

Winning and keeping customers, not just conversions is will be the defining challenge in sales during the 2020s. Every company will need a process that preserves its brand and policies while supporting the flexibility required by customer-centric personalization.

Brand Consistent, Human Creative

How can a branded organization interact with a constantly changing cast of human contributors to their sales and service experience? Since the commercial World Wide Web was introduced in 1993, the rigidity of corporate boundaries has been under assault and C-level executives have agonized over finding and keeping the best talent engaged in a sea of mobile workers.

We suggest “What’s Next.” The idea is simple: Use the brand’s existing marketing content and sales processes to analyze what is effective and racking the variations introduced by individual salespeople during their interactions with customers. A machine learning platform trained to understand the process and measure how variations impact sales outcomes watches all marketing and sales activity to find the most effective variations. Successful variations on the steps are rolled into organization-wide best practices delivered through the brand’s marketing content and sales processes.

It is not necessary to throw away the playbook your company operates with today. By launching a new level of customer-centric care using existing marketing content and sales processes, an organization can minimize upfront investments to free more resources that can be applied to filling content gaps, upgrading and expanding sales communication channels – leading toward an omnichannel customer experience – and find the optimal sales/support-to-customer ratio to maximize average revenue per customer. The challenge is deciding to change from a long feedback cycle to a short one, a finger on the pulse of your market every day.

What’s Next can be applied from the first encounter with a prospective distributor by a direct selling company, extending the onboarding process into the pre-enrollment. For example, LifeVantage, which recently launched a new Gig Economy Group platform-based app for distributors, engages prospects through an app and, at enrollment, sponsors help download and install it on the new distributor’s phone.

LifeVantage is optimizing its sales interface through the app to address every prospect, customer, and distributor touch individually, based on its existing best practices. Incorporating distributor choices about which message to use with a customer at a specific part of the funnel provides the company with guidance about where to invest in new content, improve training programs, and increase revenue.

Start Before Day One

The most successful training programs begin before the employee’s first day and last months after many companies consider their hires fully onboard. At LifeVantage, the app allows training programs take over from the sales experience through the same tool the enrollee experienced as a prospect. Depending on their experience level, the new distributor can start with more or less brand and product training – it’s their option to skip ahead to the core work the app does, to manage the customer relationship. That feedback informs training program development.

On Day One, the LifeVantage enrollee enters and starts communicating with up to 10 prospects, substantially increasing the probability of a sales in the first few weeks. As those customer interactions become more specific, such as focused on a particular product, the LifeVantage app suggests additional product knowledge training to ensure distributor success.

Throughout the onboarding, the machine learning platform observes the distributor’s sales activity and compares it to the brand’s established processes. If the distributor ignored product training that, because of customer interest is becoming a gap in their sales ability, the platform suggests additional training.

The platform also helps to compose successful text and email messages based on phrases and words that convert well for other distributors in the network.

Sponsors and management can receive alerts about changes in one distributor’s progress, which they can address through one-to-one conversations, or to performance changes across the entire network. Working from a simple dashboard, marketing, and sales leaders can create new content and messaging suggestions, testing them in real-time and receiving feedback from the field within hours.

The outcome is a comprehensive, well-aligned worker-customer experience. The two roles, worker and customer, have tended to be treated as separate experiences, but in the era of personalization, when every participant can observe and comment on the values they expect to be realized by a company, worker and customer’s experience will shape the brand’s reputation.

See You In San Diego

Gig Economy Group and LifeVantage will be presenting at the upcoming Direct Selling Association 2018 Annual Meeting in San Diego, June 17 through 19. We look forward to meeting you at the event, where our team will be exploring critical questions about the future of direct selling. Schedule a demo or reach out to meet and talk at our suite during the event.

We would also appreciate your joining our blog team for a discussion at the event about the challenges facing the industry. We will be writing about direct-selling in the weeks before DSA 2018 and would like to include your thoughts in our reports. Send email to schedule an interview.

Personalization Advantage In On-Demand Markets: Direct Sales Or Retail?

/in , /by

The economy is going on-demand, following consumers’ desire for immediate delivery of whatever they want. The sales landscape is erupting with innovation like Kilauea volcano, wiping out businesses that fail to adapt. The way out of the swath of destruction is personalization based on consistent content marketing and sales messaging. Is your company in the path of creative destruction or leading the way with technology to rapidly personalize messaging, test results, and share best practices across the organization?

Consumers have embraced personalization of sales experience, as well as product and services delivery to the home. They are also seeking more home-based work opportunities. More than 44 percent of Americans are adding side-work, or “gigs,” to enhance their income. More consumers welcome local expertise when considering a purchase and more people will be seeking part-time work. On-demand markets create both growth and distributor-development opportunities for direct selling brands. The flexible and in-home business model is becoming the norm.

Retailers, including Amazon, Facebook, and Walmart, are moving rapidly to bring the customer journey into the home, too. Amazon has begun deploying the Alexa voice infrastructure to facilitate in-home ordering, digital locks to provide secure home delivery, and, even, offer medical services. Facebook partnered last week with on-demand home services companies Porch, HomeAdvisor, and Handy.com to offer in-home services tied to products sold and delivered to the consumer. WalMart this week introduced Jetblack, a text messaging order service that will bring products to the customer’s home in hours in hours.

Direct selling and retail brands both face the onslaught of e-commerce, which is eroding the advantage of physical retail as pre-sales customer interaction shifts to digital devices. Trade and business publications frequently announce the end of retail, a claim that should be seen in context: e-commerce accounts for only 10 percent of U.S. retail revenue in 2018, according to eMarketer.com.

For example, Amazon reportedly “owns” 90 percent or more of online sales in home improvement tools, skin care, batteries, golf, and kitchen and dining accessories as of early 2018. However, as a share of the total market, Amazon converts only 10 percent of sales in these categories.

There is plenty of maneuvering room to counter e-commerce with personalized sales and service in the physical world. Resisting the change, though, will lead many companies into dead-ends. Sales experience is fragmenting due to the rise of technology, particularly mobile phones, and the consumer’s developing sophistication and dependence on social influence when buying.

Direct sellers and retailers alike will eventually follow food delivery, home services, and e-commerce into intimate relationships with the customer that start and end in the home. Direct sales companies cannot allow retail to get ahead in the race for individual customer experience. One-to-one selling remains a necessary part of the sales process.

The face-to-face advantage

Face-to-face selling is still alive and well, but it cannot ignore the digital personalization challenge. No longer will a single sales message work for every customer. Direct sellers, who enjoy the advantage of building on personal relationships, will need to craft their messages to deliver better customer experience than retail. Since retailers must first attract customers to their stories, direct-selling strategies are advantaged in the social marketplace. Distributors can develop friendships online to grow their business and forge strong local communities on Facebook, Pinterest, Instagram, and other social networks with the same level of investment of time as a major brand.

Source: eMaketer.com

The online threat, nevertheless, is existential for consumer products and services companies that fail to recognize change and invest to build personalized and one-to-one customer experience. eCommerce will reach 15.1 percent market share by 2021, claiming an additional $365.68 billion in revenue, mostly from retail stories.

Recognizing that they could be consumed by the digital lava rolling through Main Street in cities and town around the world, retailers are not standing still or playing golf in the volcanic smog. Retailers are leading the charge into artificial intelligence to win a personalization advantage, spending the largest amount of any industry, $3.4 billion in 2018 on cognitive systems to augment their online and in-store marketing, according to market research firm IDC.

The Boston Consulting Group reports that retailer expectations for personalization are very high. In a survey conducted during 2017 by the firm, two-thirds of respondents said they will see a six percent increase in revenue from personalization spending. Half of the respondent retailers with more than 25 employees said they were putting at least $5 million into the machine learning technology last year.

Both direct selling and retail will depend more on personalization to convert sales as mobile-native generations age and become the largest group of workers.

“Over 70 percent of retailers are trying to personalize the store experience. That’s never been higher,” Forrester ebusiness and channel strategy analyst Brendan Witcher told AdWeek. “The reason is because so many customers respond to it. We see nearly three out of four consumers responding to personalized offers, recommendations or experiences.”

Success starts before the sale

Direct sales’ challenge is to stay in front of digital marketing efforts by retailers, which can be accomplished by building best practices within an organization and disseminating them using automation. Pre-sales communication, starting online or in-person, must become a focus of investment to ensure messaging is consistent and relevant. Using machine learning, an enterprise content platform can analyze messages and propagate successful content and sales steps to sales representatives using mobile devices.

Brand discovery also increasingly takes place online. Direct selling marketers must develop campaigns that drive and qualify leads. Content platforms then hand leads off to representatives using automated sales process coaching to deliver all the context to present a personalized experience to the prospect. From the first to the last, every touch must reinforce the brand message to successfully close a sale and establish a long-term customer relationship.

Consumers today do more research, check facts and customer reviews, as well as depend on conversational confirmation of their buying decisions than any previous generation. Often, engaging with a brand, retailer, or distributor is the last step in the process. Marketers can respond with better pre-sales content that develops trust with consistent messaging through the entire customer journey.

Machine learning-coached sales reps can step into the digital engagement at critical moments to add the human element that establishes trust, something retailers cannot do during their sales process today. Feedback captured by representatives sitting with the customer gives the smart platform hints about how to personalize the experience, refining the suggested content to share and messages the distributor can use to move the sale forward.

“The key to closing deals is presales’ ability to shape conversations with the client to position the company’s solution as the ideal ones,” wrote McKinsey’s Homayou Hatami, Candace Lun Plotkin, and Saurabh Mishra in the Harvard Business Review.  “This approach is not about developing a ‘smoke and mirrors’ pitch, but rather investing the time to have a deep understanding of the client’s needs (met and unmet) and then highlighting those elements of the solution that can address them.”

The foundation for a consistent brand message begins with mapping every touchpoint in the sales journey, from pre-sales and discovery through content marketing and sales process steps. The “attribution modeling” process allows management to identify what it expects will happen at each step and, using a machine learning-content platform, rapidly test and revise messages. Instead of launching content one or twice a year, then waiting to see its impact on sales in quarterly or annual results, it can be adjusted as fast as software updates are today.

The speed of software is the new pace of sales in the era of personalization. Companies are beginning to adjust to this accelerated communications cadence, and the tools for in-home personalization are catching up to web-only interactions. The combination of digital and personal engagement is a breakthrough moment in sales.

Personalization is the path out danger for retailers and direct sellers that don’t want to wait for the lava of change to erupt under them. For now, the one-to-one selling community has a sustained advantage over retailers who must attract the customer to their stores. If retailers’ investment in machine learning and personalization goes unchecked, direct selling could fall behind despite their strong foothold in consumers’ homes.