Three Data-Driven Questions Sales Teams Should Ask In 2020

sales data analytics

In a world where every type of sales initiative seemingly starts online, many traditional sales practices still hold true.  While many expert salespeople focus on things like “ABC” (Always. Be. Closing.) and making eye contact, the modern sale starts before the first contact.

How you source your leads, interpret their quality and mine them for data insights will determine just how successful your sales efforts and campaigns end up being. Here are three questions to keep in mind as you build your sales analytics strategy for your sales team.

What sources of data can we be capturing?

Before any fancy algorithm or expensive SaaS subscription can be put to use by your sales team, ask yourself where the source of your data originates. For example, if you are using expressions of interest in a marketing survey as a data set for forming your potential customer profiles, you might get a completely different result than if you were to profile an existing customer base of an older product your own company offered or the product of a competitor.  Too often companies create a metric of the type of data they source and then only work on improvements to the analysis of that data post collection. There are limited potential permutations of insights into any single data source but changing the source can give you an unlimited field of new ideas and directions for your business and sales strategies.

For instance, an automobile sales team that calculates close rates and creates forecasts based on inbound calls and walk-in traffic. They are limited to the amount of inbound traffic they get, a factor that is prone to seasonal variance and additional external forces such as competition running promotions or even highway construction reducing visibility. This dealership could diversify the source of its forecast data by adding in Mailers, Outbound calls and showcasing its inventory at auctions.  It could keep intact it’s calculations on close rate and focus on holding sales associates accountable on a per lead basis, but also give additional control and accuracy to the company-wide initiatives by diversifying the data source that underlies these forecasts.

Are you moving at the speed of your industry? 

Even for teams that do an excellent job of diversifying their data set sources, the problem of “Old Data” can rear its ugly head. If you are only forecasting once a quarter, you risk relying on information that is already outdated. Good historical data sets used to be things that companies could use for annual planning and evaluated once a quarter; now in 2020 companies are finding that trends can change on a daily basis. This is especially true in the consumer industry where demand is fickle and often influenced heavily by a singular trend or idea catching the public wave. Selling to large enterprise accounts might be a longer lead time, but even the most recognisable of fortune 500 companies are investing heavily to become more agile.

It is necessary to internally evaluate if your team is consistently updating and refreshing its forecasts instead of relying on the same milestones it may have fallen into the habit of using.

Are you investing in understanding sales & business insights?

It can be daunting to approach “big data”, and many members of your team might be using forecasts or insights they don’t fully understand. Setting time aside regularly for your salespeople and managers to do continuing education around the types of forecasts and data your industry uses the most can pay massive dividends. By setting aside a time where team members have space to gain familiarity and comfort using the expensive data-driven tools you provide, you can expect to see an increase in the actual reliance on those tools. Here we explore 6 steps to improve data literacy to deliver business value

With these three questions in mind, you can kickstart your journey to better sales analysis and forecasting. For more information, check out our ebook on Providing Sales Teams With Relevant Information To Do A Better Job using data and analytics.  

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