Recently, Accenture conducted a fascinating study on asset managers. One finding that stood out was that almost all managers agreed that investing in data analytics and driving data-driven growth is their highest priority. ¯
Data today, in fact, is critical to investment success to the extent that it is often referred to as the competition’s single biggest differentiator. Although, one can make the case for any competition, not just finance or investment research but marketing, HR, customer support, and sales. Especially sales. ¯
Especially sales because it has been completely transformed by the advent of data analytics. McKinsey, for example, found that the use of data led to an almost twofold increase in sales and profit. Data-driven decision-making, they agreed, ¯is undoubtedly the single biggest difference between the best and the rest. ¯ ¯
The question is, what is it about data analytics that fuels sales success? ¯ ¯
Using sales analytics to boost your business
In the absence of data, it is hard to identify and convert prospects, let alone do the ¯rest. Because enterprises can either make sales decisions based on instinct, often misidentifying prospects, wasting limited enterprise resources, and taking other costly risks. Or, they can act upon insights based on evidence, insights gleaned from ¯data analytics. ¯ ¯
We call this sales analytics ” the practice of leveraging sales insights to make sales-critical decisions. ¯
The insights are generated by collecting and analyzing a wide range of data. Sales analytics is fed data not just from sales, but marketing, finance, customer relationship, and any place where a prospect might leave its fingerprints. ¯
Okay, so how does all this better the 4 most critical sales decisions you make? ¯
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Conversion: ¯Sales analytics uses historical data constituting transactions, marketing engagement, website or app tracking, and other relevant data to precisely identify customer expectations ¯and pain points. A sales team can address the pain points to make conversion more likely.
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Personalization: ¯If data is abundant and high-quality, and analysis is meticulous, we can understand human behavior at an individual scale. While this is the stuff of the future, we can certainly cast the spotlight on very tiny groups. What that means is, enterprises can very accurately curate not just sales pitches, but marketing campaigns and customer support communication for a very narrow demographic. They can then divide the larger demographic into smaller ones and do this for each of them. Conversions become more likely because offerings are more personal and meaningful. ¯
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Cross-selling: ¯When enterprises analyze sales data, ¯they look for patterns or trends that depict a relationship between two or more purchases. The goal is to anticipate future needs accurately. ¯Because, instead of bundling together ¯unrelated ¯products/services, enterprises can leverage data to reliably drive up/cross-selling. It is worth noting that this creates value for both enterprises and their customers. Enterprises make more sales while their customers are offered products/services that truly complement each other. ¯ ¯
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Retaining your customers: ¯When your customers’ deepest needs are addressed, and they are offered just what they need, in precisely the way they like, they turn loyal. They come back for more. ¯
Why most fail to leverage sales analytics
Accenture’s fascinating study also found that nearly 3 out of 4 asset managers believed that the biggest challenge to adopting data analytics solutions is the lack of data-centered skills. ¯
Well into the era of big data, data ¯illiteracy is still prevalent. An enterprise, even small scale, generates mountains of data every day. But it takes data literacy to determine what data qualifies as relevant data. In this case, data that genuinely offers insights into future sales risks and opportunities. ¯
And that demands skill. It demands years of expertise. Data literates excel at statistical analysis, mathematical modeling, and alsoand, coding, among other advanced big data skills. ¯
The point is, when we think of data analytics, we think of advanced computers. And that is reasonable. Computers are the means toof obtaining sales trends. But sales trends will remain just trends if we don’t invest in skilled analysts. It is the analysts that interpret the trends, make meaning of them. Only then can trends be actionable. ¯
We can’t leverage data analytics to fuel sales if we don’t understand sales data. Because sales insights are only as good as the sales analyst who interprets them.
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