Business problem
Data, data, data. Seems it’s all you hear about in the business sphere, and with good reason. After years of being told how to collect more and more data on both current and prospective customers, we’ve reached a tipping point in which we have so much data, we don’t really know what to do with it. There are even warehouses for it now!
So, most revenue executives just continue to collect more data without understanding why or how best to apply it to their sales efforts. Often, this leads to the generation of insights that quickly become stale, or insights confirming their own bias. Sales teams end up reverting right back to tribal knowledge and instinct. Because they are drowning in data, they have no real way of knowing which prospects have the highest probability to close. There’s very little in the way of systems or methods to tell us. With both science and art, where do you point the canons to get the highest return?
RVP’s solution:
The key to improving sales results is improving the real-time accuracy and application of the data you currently have and are collecting, rather than falling into the “more is better” trap. Here is how this approach works.
Use agility and data science to identify the best opportunities
Instead of traditional segmentation in which insights are refreshed annually at best, agile segmentation allows for a continual update of customer data. When combined with machine learning, insights become more current and accurate. The two components of agile segmentation are:
- Potential ICP fit is determined by grouping accounts and prospects into cohorts, based on third-party data, such as company revenue, industry and descriptive heuristics. From there, “like” companies can be analyzed to determine what they typically purchase as a percent of revenue to provide a proxy for what prospects or underpenetrated customers are capable of spending on your business’s solutions.
- Accounts then receive a score driven by their revenue potential and their likelihood to purchase. The account score models are often developed using a combination of firmographic and technographic indicators to identify patterns in your existing customer base, which can be observed in prospects. The presence (or lack) of these markers indicates that a customer has a greater propensity to receive value from your product or service.
Make decisions based on real-time insights, not judgment calls
Rather than choosing which accounts to upsell or prospects to go after based on past experience, leverage real-time visibility into how sales efforts are performing to tweak your approach as you go. You can also update your Ideal Customer Profile — a multi-variable, structured description of a company’s ideal target for marketing and direct selling efforts — as market dynamics change or new products are launched. The results will speak for themselves: more wins, larger deals, and an overall faster sales cycle. Your highest quality marketing efforts can be targeted at the accounts that have the highest revenue potential and probability to close. Your marketing team can finally stop wasting time and money on low-quality prospects.
