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How Well-Documented Sales Processes Spur Sales Creativity

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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?

 

Mapping The Sales Journey: Machine Optimizing Customer Experience

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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

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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.

Getting Started With Machine Learning for Sales Content

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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.

LifeVantage: Machine Learning In Direct Selling

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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.

 

 

 

Product Knowledge Is Retention Power

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As companies like Best Buy add local service and home consultation, direct sales organizations must train distributors on every aspect of the products they sell to remain competitive. The personalized in-home selling trend will reach every corner of the consumer market over the next five to ten years, placing the direct selling one-on-one customer relationship advantage in mortal peril. Product knowledge training is the basis for improved sales, faster product and business process innovation.

But most companies struggle to keep their reps trained, let alone ingest the ongoing feedback from the field that can ignite higher revenue and profits. Too often leadership’s expectations are based on ageing practices that are obsolete in the era of cloud services.

As selling becomes personalized, mobile, and mission-based in the hands of values-driven generations, the tools needed to successfully mine the data created through these interactions are the highest priority for a sales organization. In direct selling, the imperative to gather and analyze feedback from representatives is rising in the face of aggressive retail investment in personalization, not to mention improving distributor retention rates in an increasingly mobile workforce.

Product knowledge is the foundation of customer engagement and trust. “87 [percent] of consumers said they would be unlikely or very unlikely to make a repeat purchase with a retailer that provided inaccurate product information,” according to Shotfarm.com, a Chicago content management company. Each sales rep who flubs a fundamental product knowledge question because they are selling outside their area of competence due to poor coaching runs the risk of permanently losing a customer for the brand.

Combining content management with machine learning to deliver personalized product training to salespeople in the field redefines the challenge of keeping product knowledge up to date. “Smart” tools assist in building product knowledge and coach salespeople toward the products with which they are most likely to succeed. As marketing, training, and sales content libraries grow, machine librarians will be poised to help distributors tell a consistently expert story about products.

Augmenting a sales rep with appropriate content and sales process coaching ensures a brand can deliver the right content to a curious prospective buyer at the right time.

Today, sales and marketing leadership is challenged to rethink the training process to accelerate sales conversion rates while building higher customer retention rates based on distributor engagement in the branded selling process. Every salesperson-customer relationship is unique and companies today must treat them as such. This is a new opportunity, one born of the information era and utterly foreign to traditional sales strategy that uses one training program across the entire company.

Starting with achievable expectations

It is not necessary to try to train everyone in an organization about every product in the same way. Instead, training is conceived as a personalized experience that addresses the specific learning and selling styles of the salespeople in the field. This groundwork lays the tracks to personalization of customer experience.

Tracking sales activity using automation turns organization-wide product knowledge training into a tractable problem. Since direct-selling representatives tend to specialize in niche areas within a brand’s product portfolio, targeted training allows sales management to fine-tune product knowledge investments. Knowing precisely which products a direct sales distributor is trying to sell, machine learning enabled content platforms can identify knowledge gaps and serve up training that addresses the individual distributor and their customer’s needs.

Instead of aiming for 100 percent product knowledge across the company, the platform allows leadership to treat product knowledge challenges in isolation, using the sales coaching process to move distributors toward complete competence in their area of interest. People in the field experience less frustration because they receive more information that is relevant to them, which leads to a higher retention rate among distributors. That product knowledge competence extends to the customer experience as distributors become deep experts who can answer every customer question quickly and accurately.

When great distributors stay, they keep their customers with the company.

Diane Valenti writing for the Association of Talent Development suggests managers develop “return-on-investment” expectations as a baseline for training investments. “Assuming that sales reps are applying what they learned, you can measure whether what they are doing is getting results using sales metrics that you already have in place,” Valenti said. “Don’t invent anything new.”

Direct sales companies can start out with the content and process they have today and modify it, rather than try to reinvent themselves from scratch. Existing training video, audio, and documents can link to assessments of how well a sales rep has learned.

As a starting point, marketing and sales teams in direct selling organizations can base assessments of distributor competence on individual sales success, not just the all-up sales results for the organization. By capturing more feedback from each rep, such as asking them review questions a part of a daily or weekly briefing delivered to their phone or having them record customer interest level after each conversation, leadership can move quickly to refine training programs at the individual content asset level to improve overall performance.  This investment leads to improved conversion rates and average revenue per customer as the likelihood customers will become dissatisfied due to knowledge gaps in the organization is reduced because each representative is well trained.

Resisting investment in training is costly. Ignoring feedback from reps can be deadly. The Center for American Progress estimated that organizations with poor training see $13.5 million in costs due to poor skills, employee disengagement and higher turnover. For a direct selling network with 20,000 distributors, the direct costs and lost sales could be as high as $270 million annually.

Product knowledge training based on extensive feedback and personalization is a source of product and marketing ideas, not just a means to sell.

The ideas captured by listening intently to reps responding to customer needs can be used to redesign products and improve the customer journey. Insight at the field level will determine which companies win. Boston Consulting Group research in 2015 found that fast innovators are more successful, bringing new features and categories to market more quickly to generate as much as 30 percent of revenue annually from new products. Survivors of creative destruction don’t eke by, they thrive.

Successful training based on knowing “What’s Next?”

The training process itself is the map to organization-wide improvement.  An attribution modelling strategy systematically allows a company to lay out its expected sales journey and compare the resulting training and sales feedback with initial assumptions to pinpoint content and training gaps. The steps in the sales journey become a template for “What’s Next” in the representative’s day long after they have complete product knowledge. The same information used to train a rep can be repurposed to support their selling.

Product knowledge training linked to sales success or the setbacks experienced by reps in the field is also a leading indicator of customer issues. Following up on customer conversations with training material related to the engagement keeps the rep focused on learning and providing even more feedback about a product’s perceived value.

Automation leaves managers more time for understanding feedback, rapid, intuitive analysis of sales data, and improved content programming and product development. They can deliver more of what the field needs: Guidance and better resources. A What’s Next-based sales platform managed by a machine learner can experiment with the content delivery process, analyze the impact of small changes on conversion, and translate the findings into new sales journeys, as well as mine feedback for delivery to the product team.

A content platform with machine learning keeps the information stream to the distributor concentrated on what drives sales success for one person and one product, or an entire brand with minimal human oversight.

By making product knowledge the fulcrum of customer engagement, with personalized training for the distributor to help them move to better outcomes, a direct selling company reinforce its one-to-one relationship advantage in the market. Ultimately, the What’s Next design anticipates the customer’s questions, identifying their needs to give the representative greater insight into what drives the sales decision for each prospect.

The question every sales leader must confront is: “Are you confident that your sales team knows everything about your products that the customer will want to know before buying?” The answer at each step is found in laying out what the expected next step toward a close and measuring for success after each engagement.

The 70,000-ft. view of sales results is no longer sufficient in the personalized marketplace; managers must use automation to move along with their salespeople at the edge of the network.

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 an email to schedule an interview.

 

Closing A Critical Gap: Marketing and Sales Alignment

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The central role of one-to-one relationships in direct selling places unique demands on marketing and sales leadership. Together they can succeed spectacularly but misalignment reduces conversion rates, wasting valuable investment in lead generation and customer experience.

As sales practices evolve to emphasize pre-conversion communication and trust-building through mobile apps, direct sales companies can leverage their unique human connection with customers for unprecedented advantage over retail and online brands. Historically, however, marketing and sales have competed for resources within network marketing organizations instead of working together to establish and disseminate best practices.

Competition between marketing and sales teams opens a costly divide within a company that limits the ability to develop and share best practices. If marketing messages fail to move the sales process forward, valuable leads are lost. Sales teams in direct selling often rely on their marketing partners for training content, company messaging to distributors and, in many cases, sales collateral designed to convey overarching value propositions which are not communicated consistently during the sales process. Without iron-clad data to prove replicable sales success or that points to where conversions are lost, the quest for change can become futile.

Beyond creating discord in the messages prospects and customers receive, the struggle for dominance within direct selling companies hits the bottom line hard.

Organizations with Strong Marketing and Sales Alignment Outperform Their Peers in Current Metrics. Source: The Aberdeen Group

A lack of alignment between marketing and sales messaging results in 14 percent lower achievement of sales goals annually and lowers customer retention by 11.1 percent, the Aberdeen Group reported in April 2017. When sales and marketing collaborate successfully, Aberdeen Marketing and Sales Effectiveness analyst Andrew Moravick writes, companies “grow revenue at 64 percent greater rate” than poorly aligned organizations.

As retail and online marketers increase spending on personalization in 2018 by 54.2 percent year-over-year, according to technology market research firm IDC, direct sellers need to tighten marketing/sales alignment to keep up with the best brands in the world.

It is time to augment direct selling content and customer relationship management systems with machine learning, often referred to as “Artificial Intelligence.” These tools arm direct-selling distributors with the right content for a specific customer at the most opportune moment in their journey.

Customer-centric, mobile-first context is king

Content rules when it is delivered at the right time. Content without context, like a confusing value proposition, turns off the customer.

Sales has changed, placing a premium on providing pre-sales information based on situational awareness of the customer’s needs. As companies develop huge content libraries necessary to support a rich customer journey, machine intelligence can serve as a context-aware librarian that retrieves the message, video, or collateral needed. The salesperson’s intuition can blend seamlessly with a machine learning platform if the final choice is left to the human in the field.

In addition to targeting the customer’s needs, a next-generation direct selling platform requires awareness of the salesperson’s strengths, product knowledge, and relationship with a prospect. Depending on the level of trust established between representative and customer, different content and messages can save or close a sale.

Marketing and sales leaders should work together using an attribution modelling strategy when starting out with content platforms using machine learning. Harvard Business Review authors Werner Reinartz and Rajkumar Venkatesan write that the attribution modelling approach “allows companies to attribute appropriate credit to each online and offline contact and touch point in a customer’s purchase cycle, and understand its role in the revenues that ultimately result.”

Leadership can begin by identifying a single target customer persona, then mapping out their ideal customer journey and the rules for handling each critical engagement expected to move the sale forward. This exercise compels marketing and sales leaders to talk about the customer-salesperson relationship based on a mutual understanding of the company’s customer persona, the target’s needs, and established product value propositions.

The extra ingredient that transforms this work into an alignment tool is the use of measurable events within the marketing engagement and sales journey to establish accountability for each team.

Growing measurable best practices

A high degree of humility is required in the face of real-time reporting. Feedback from the field shines a light on critical content marketing gaps, as well as a faulty sales strategy. Organizations can use machine learning-augmented content platforms to move from annual or semi-annual content development and sales planning to a quarterly or faster pace to optimize their sales processes.

At first, the mapped process represents a collective but untested agreement. With the help of a machine learning algorithm that applies the rules to find, contextualize, and deliver marketing content that supports the sales process. Real-world feedback generated by salespeople in the field will tease out multiple customer journeys. After that a fine-grained range of personae can be addressed with targeted content, expanding the addressable market without high incremental content production costs.

When designing a target customer journey, the teams can start with an inventory of existing content and map it to key conversion points in the sales process to establish accountability for message consistency. Sales leaders can be confident that poor content targeting assumptions during the planning stage will be clearly visible in the resulting metrics while marketers will be able to point out how content is misused in the field. These trade-offs can energize the entire company.

The attribution modelling strategy also gives leadership the ability to assess how marketing investments impact revenue generation. Simple rules for attribution can be used by a machine learning algorithm to adjust messaging cadence, the order in which content is presented at key touchpoints in the customer journey. As distributors add their feedback about customer interest and objections through a mobile app, the algorithm can be enriched to deliver insights that drive an organizational emotional intelligence unprecedented in sales.

Prior to cloud-based big data services, the initial rules and data generated by executing the rules would have required predictive analysis to be useful, but a machine learning algorithm can accelerate and simplify the process for management.

Customer feedback drives rapid organizational optimization

As distributors choose different messages and adjust the language they use when communicating through a machine learning-enabled content platform, the algorithm watches and propagates the what works best to improve outcomes across the entire organization, from headquarters to the field. By testing relentlessly variations in the order content is presented, suggesting new text through email, SMS, and social interactions, sales and marketing leaders assisted by a machine learning platform can evolve best practices informed by actual distributor decisions.

Moreover, poor sales performers in need of more training, specific types of coaching or improved product knowledge will be identified more easily than in direct selling’s largely manual sales reporting process. An investment in content targeting exposes the opportunity to improve individual distributors’ sales skills, as well as enrollee retention and sales conversion rates.

Sales and marketing alignment grows revenue overall and keeps customers buying. The Aberdeen Group reported that “Best-in-Class” companies, which see consistent year-over-year reductions in the length of their sales cycles and improvement in company sales quota achievements, “have complete or strong marketing and sales alignment, compared to just 45 [percent] of All Others.”

Direct selling companies that embrace machine learning platforms must be prepared to iterate based on the discoveries of weaknesses in their initial, idealized process. The rewards are numerous, from lower training costs and higher distributor retention rates, growing revenue and long-term customer engagement.

As data accumulates, each customer engagement, in email, in-person, online, broadcast, and the phone is revealed to be more, or less, important than leadership expected. Content and messaging gaps will become obvious because conversion rate changes are immediately reported by the system.

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.