Power Your Sales Transformation with Analytics
The lexicon of analytics — “big data”, “AI”, “machine learning”, “predictive models”, “next best action” — has entered the vocabulary of business executives and sales leaders. This is absolutely a sign of things to come: analytics will permeate sales — and marketing, service, operations, … — all aspects of modern business will eventually be enhanced through analytics.
Sales, however, is a unique function. We talk about it in almost mystical terms, equal parts sorcery and science. Individuals and entire teams have up-years and down-years, sometimes impossible to explain from economic factors. It’s sensitive to change: small disruptions to sellers and sales process can have big impacts on business results. Despite enthusiasm for analytics, business leaders are justifiably wary of introducing process and technology changes. And adoption of analytics will be transformative — it is a sales transformation.
This is the first in a series of articles providing practical guidance for your analytics journey. Here I outline how your analytics program can be structured to enable sales transformation; future installments will show how sales process and management can be charged with analytics.
An Analytics Program To Fuel Sales
Data analytics is a well-established field and plenty has been written about implementation. However, traditional approaches tend to fall flat in sales — due to the strong culture of sellers and the very real risk of distracting these revenue-generators. The key to success is aligning your program with your sellers. Here are 6 tips for structuring your analytics program to power sales transformation:
- Design from the bottom up. Conventional wisdom holds that we build analytics first for executives — because they can never get enough data and they hold outsized influence in the organization. However, executives and salespeople worry about different things; the insights that executives are seeking have little relevance to the daily business of closing deals and converting prospects. Your analytics program should focus first on salespeople… because even small improvements to everyday decisions compound across the sales team. These visible results will get the attention of executives — who will then help amplify adoption (and introduce their own requirements).
- Govern from the bottom up. Forget traditional steering committees and executive governance; form an Analytics Working Group comprised of 10–15 sales and sales support personnel. This team will be the beating heart of your analytics program — setting scope, objectives, and priorities — and the face of the program to their peers and seniors. When recruiting for the AWG, look for your most forward-compatible sellers — the ones who are always looking for ways to improve, are able to quickly integrate new techniques into their professional practice, and are comfortable “failing fast”. These aren’t data scientists or business executives; these are people who are ready to apply analytics in the deal cycle.
- Dream with the leaders and deploy for the adopters. Your organization will eventually self-segment into analytics leaders, analytics adopters, and everyone else. Stay focused initially on the first two groups; their successes will ultimately attract the third. Exclusivity here is a virtue… Try to place your future leaders on your Analytics Working Group. Then establish an active beta program for early adopters — with participation by invitation and a commitment to provide feedback. Design each new feature in collaboration with the AWG, then place it in beta for more varied and specific feedback. Rapidly integrate this feedback and in no more than a month or two roll it out to the broader user population. A key benefit is that your early adopters will already have field-tested the feature in beta, and will provide success stories that will aid your rollout to the greater population. Over time, interest in the beta program — early access to revenue-enhancing features — will grow, evidencing an engaged user population.
- Build a strong user community. Use a digital collaboration platform (like Salesforce Chatter) to foster an engaged user community. Post your roadmap, release notes, training materials, job aids, and tips/tricks; encourage users to post their questions, experiences, and best practices — and help each other. Have your analytics team review their posts, add guidance, and ensure the questions get answered. Promote success stories and profile your power users. This will supplement your structured training and communications, and you’ll see that behavior change ultimately propagates faster through the community than any other channel.
- Empower Citizen Data Scientists. Most sales organizations include some very clever analysts, often in sales support, assistant, or junior sales roles. These individuals facilitate your front-line sales team by building lead lists, coordinating local sales campaigns, identifying cross-sell opportunities, etc… usually with desktop tools like spreadsheets. They’re data analysts in waiting. Include them in your analytics program by offering training and a sandbox to work in, and support them in building their own analytics. Then host show-&-tell sessions and harvest the best ideas for your program; recognize and promote these contributions. By enabling these collaborators you can vastly expand your analytics development capacity, introduce fresh perspectives and unexpected ideas, and advance their careers.
- Jump-start your data management program. Analytics expose everything — all the holes and faults in your data. Poor data quality may in fact be the biggest risk to your analytics program. If you don’t have a formal data management/stewardship initiative, get started on that ASAP. If you do, ensure its focus and priorities are aligned with those of the analytics program. Data management isn’t flashy, but it underpins everything you hope to accomplish. As you introduce new data points into your analytics program, begin with a quality assessment and lineage analysis to help anticipate problems and avoid analytics credibility crises.
The insights shared here were culled from a successful sales analytics program at a global financial institution using Salesforce Einstein Analytics.
This article was originally published at https://www.linkedin.com.
