How to Get Sales Reps to Adopt and Crave Predictive Analytics

by   |   June 30, 2014 5:30 am   |   0 Comments

Javier Aldrete1 How to Get Sales Reps to Adopt and Crave Predictive Analytics

Javier Aldrete, Senior Director of Product Management, Zilliant

Business intelligence alone is no longer good enough to boost company performance. As such, many companies are moving to analytics, particularly when it comes to trying to help their sales teams sell more. But despite managers’ best efforts, it is unlikely that sales reps will ever really adopt analytics — at least the way analytics is most commonly deployed within organizations.

For the majority of companies, their first step into the world of analytics is with descriptive analytics, or hindsight analytics. By definition, descriptive analytics is inherently backward-looking and provides little to no value other than reporting on what happened in the past. It gives no indication of how to improve performance in the future.

Predictive analytics, on the other hand, promises many benefits to B2B sales organizations.

As the market shifts to a more progressive form of intelligent guidance, the success of predictive analytics will hinge on the approach companies take to encourage adoption by sales reps. Like any new technology roll-out, if sales reps don’t buy in to the change, the deployment is likely to fall flat. Yet, providing sales reps with tools and reports actually hinders adoption rather than encouraging it. What most companies don’t realize is that sales reps don’t need access to the analytics, they simply need the guidance generated by it.

Why Sales Reps Abandon Analytics

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B2B sales organizations are faced with massive decision complexity. Tens of thousands of customers, hundreds of thousands of products, and mercurial factors such as new products, new markets, and competitive pressures result in hundreds of decisions for one sales rep to make each day about which customers to call on, what products to sell, and what price to quote.

Company leaders are well aware of this decision complexity. In an attempt to help, they provide reps with extensive reports that contain customer-spend analysis, customer churn trends, and high-level price guidance about margin targets. While this effort is well-intentioned, adoption rates are still abysmal and initiatives typically fail, primarily for these three reasons:

Salespeople aren’t analysts. They don’t like or read reports. Sure, your top reps may use the reports, but most salespeople simply ignore them.

Reports are backward-looking. The reports can tell reps what their customers purchased in the past, but they don’t provide explicit guidance as to where reps should spend their time in the future. It’s certainly helpful to understand historical customer data, but a backward-looking report won’t help employees make better decisions in the future.

Manual approaches can’t scale. To manage the complexity and perform thorough analysis on each customer and product in the entire book of business on a weekly or even monthly basis, you would need an army of analysts. As a result, most companies are able to address only the top 20 percent of customers and products, leaving the remaining long tail to guesswork.

Sales reps don’t want to spend their time sifting through reports. They want to spend their time doing what they do best: selling. They crave the answers to their questions, not just the data and analytics behind the answers. Reps need sales guidance that’s immediately actionable and tells them explicitly where to find the opportunities most likely to result in a win.

How to Give Reps the Guidance They Crave

It’s possible to deliver analytics-based guidance to sales reps that they will actually use. The data to generate that guidance already exists. By applying predictive science and algorithms to transaction data and delivering the output as actionable guidance to sales reps, you can halt customer defection, grow organic revenue, hit or exceed margin targets and gain share. In essence, it’s all about the leads and the quality of pricing guidance delivered to the reps.

For example, to keep and expand wallet share with existing customers, predictive models can find the purchase patterns in the data and uncover the retention and cross-sell opportunities for each account. These actionable opportunities can be delivered to sales reps to help them understand what each customer should be buying from them.

A predictive model also can help set specific, market-aligned prices that account for every selling circumstance. The model allows you to understand how those pricing strategies will impact future P&L performance before prices are put in-market. You can then deliver market-aligned price guidance directly to your sales reps in the quoting systems they use today.

If predictive analytics are leveraged this way, it removes the need to ask sales reps to decipher reports and gives them specific answers about where to spend their time and how to quote a price that won’t lose the deal while achieving the company’s P&L objectives.

How to Tackle Adoption Challenges

Generally speaking, this type of approach to deploying predictive analytics helps to minimize adoption challenges. However, sales reps still have to trust and use the guidance. The first step to aid in adoption is to ensure the guidance is easily accessible to sales reps in the systems and tools they already use today: e-mail, mobile devices, CRM, etc.

Sales reps might believe their “gut feeling” is more reliable than the sales guidance, so expect them to be resistant at first. Here are a few tips to help with adoption:

    • Clearly articulate the benefit to them as individuals as well as to the organization. Address the “what’s in it for me?” question

 

    • Consider using rewards and recognition programs to drive adoption of the guidance.

 

    • Ensure that sales managers are engaged and incorporate the guidance into the overall sales process.

 

    • Communicate early and often about quick wins and successes.

 

The sales reps’ experience is still highly valuable, and they should view the predictive guidance as a “personal analyst” that simply helps them be more effective, ultimately helping them and the company make more money. Analytics can boost company performance, but only when deployed in a manner that is actionable for employees making decisions on the front line.

Javier Aldrete has more than 16 years of experience applying advanced business intelligence and predictive analytics to solve business problems in multiple industries. As senior director of product management at Zilliant, Aldrete is responsible for the company’s sales optimization product offerings’ vision and road map.


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