4 Lessons for Optimizing Sales Performance with Data Analytics

by   |   November 12, 2015 5:30 am   |   1 Comments

Mike Saliter, VP, Global Industry Solutions, Qlik

Mike Saliter, VP, Global Industry Solutions, Qlik

Every sales organization wants to succeed and gain an edge on the competition. A logical tactic is to look at the organizations that outperform their peers and analyze what they are doing differently. This led Qlik to sponsor a custom research project with the Economist Intelligence Unit (EIU).

The research included a global survey of 550 senior sales executives as well as executive interviews to examine how organizations manage the sales process and whether the use of self-service data analytics results in better sales performance. The findings are detailed in this EIU report titled, “Unique selling points: Separating sales leaders from the pack.” Here are the four main takeaways and perspectives from the report:


Sales Performance Is a Top Priority, but Organizations Need Data to Support it

The report found that 79 percent of companies said sales performance was “much more important” or “somewhat important” compared to other business objectives. This clearly indicates that sales excellence is top of mind and a core focus for most organizations. But only a quarter of the respondents felt they were very good at it. When asked what the biggest barriers to success are, the most common answer, after resources/skills, related to data. This comes as no surprise because many organizations still rely on instincts and manual spreadsheets to run their business. While there’s so much market buzz and hype around big data, the reality is that most sales organizations still struggle with the basics. Most organizations still do not have a solid handle on their existing internal and external “little” data, let alone big data.

The Best Performers Leverage their Data Assets Extensively

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A whopping 97 percent of companies that indicated they were “very good” at executing on sales objectives had real-time, self-service access to customer or to account data. Within that group, 60 percent said they access BI reports one or more times per day. This clearly shows the companies that succeed are also frequently leveraging their data for analysis.

This begs the question: Why don’t more companies make self-service analytics available to their entire knowledge worker community? Far too often, BI and analytics is reserved for the anointed few who have deep training and expertise in complex tools. But in today’s world, there are a multitude of easy to use, visually appealing self-service tools that have leapfrogged the traditional BI offerings in terms of total cost of ownership, speed to deploy, ease of use and adoption.

In this analytics customer case study, UK-based Lush Fresh Homemade Cosmetics shares how it deployed analytics to every employee, and more than 70 percent of its staff accesses it daily for sales and supply chain analytics. The company achieved a quantifiable ROI of more than £1 million through optimized stock inventory. There are so many additional ways to calculate ROI tied to broad user adoption of analytics, including: time savings, increased productivity, improved cross-sell/upsell, better customer service, decreased fees, avoided risks, and optimized pricing.

Another key question organizations should ask themselves: Why are we focusing so much of our attention on storing and aggregating data but so little time on actually analyzing it? Standing up a Hadoop instance or building an enterprise data warehouse are nice technical accomplishments, but I’d argue they provide zero value on their own, and the real value comes to life when people analyze the data in order to make better business decisions. The 80/20 rule could be applied here: If 80 percent of the time is currently spent on storing and aggregating data and 20 percent of time is spent analyzing it, a good goal is flipping that number to spend 80 percent of the time analyzing and just 20 percent storing and aggregating. This would significantly increase the value and ROI of data investments while also helping to define future data requirements.

Data Accuracy and Integration Are Hugely Important

While leveraging data is important, the old saying of “garbage in, garbage out” applies here. Data accuracy is just as important as data access. The study found that 53 percent of respondents felt data accuracy was the top requirement for their sales data analytics applications. The next most important feature was integrating with existing systems (38 percent). This speaks to the significance of avoiding silo applications and making sure sales analytics is tightly coupled with current data sources. If people can’t trust the numbers and don’t know where they come from, then adoption is sure to suffer and any data analytics project is doomed to fail.

Pervasive Adoption of Analytics Involves Everyone, from the C-suite to the Sales Rep

Use of analytics in the C-suite is not a pipe dream. The EIU report showed that a third of the organizations that said they were “very good” at executing on sales objectives had extensive data analytics use by their C-suite. By contrast, others had just 19 percent use of analytics in the C-suite. One of the best ways for organizations to embrace analytics is by seeing their top executives not only mandate it, but also use it. Analytics is not just for the top-floor execs, and many progressive organizations see the value in extending their analytics use all the way to the front line sales people. The more informed that knowledge workers are, the better their decisions will be, regardless of role.

The EIU study leaves no doubt that greater access to data, more frequent use of analytics tools, and analytics adoption across an organization lead to better sales performance.

If you are interested in a deeper analysis of the EIU study and want to hear directly from research lead Charles Ross, please access this on-demand webinar. Additionally, the survey data are available in this interactive online app.

Mike Saliter is Vice President, Global Industry Solutions, at Qlik. Mike has nearly 20 years of hands-on experience in the Business Intelligence and Analytics market. At Qlik, he is head of the Industry Solutions organization, which comprises global industry experts who lead Qlik’s industry and functional go-to-market initiatives spanning sales, marketing, solutions, and partners. Prior to joining Qlik in 2011, Mike spent three years as Business Unit Executive for global Industry Solutions within IBM’s Business Analytics division. He also worked six years at Cognos in various presales, sales, and marketing leadership roles. Prior to Cognos, he consulted for Accenture on various BI-related projects. Mike has a BS in Engineering from Virginia Tech and lives in the Washington, DC, region.

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One Comment

  1. Amara Heslin
    Posted March 28, 2017 at 7:05 am | Permalink

    Insightful article. I do agree with you that data accuracy and integration is very much important in sales performance with relation to data analytics. Analytics are SEO are two of the most (e.g. Google Analytics) important things in internet marketing. Being knowledgeable about those two could help you big time in your internet market venture.

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