Why does sales analytics go wrong?

7th Sep 2015
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An increasing number of organisations are adopting sales analytics to drive sales performance. The motivation is simple: better measurement of sales team behaviour enables better overall management of the salesforce.

Nonetheless, sales analytics implementations aren’t an unqualified success. And even those organisations that have diligently tracked their sales activities and rigorously deployed sales analytics tools can find that they still lack the insights they require to improve sales performance, or have the insights but can’t translate them into improved processes and practices. So what’s going wrong?

Let’s take a look at some of the most common reasons that sales analytics fails to deliver.

Not asking the right questions.

Sales leaders need to link their line of questioning and data exploration efforts back to specific business objectives. Without doing so, it’s very easy for business users to spend too much time wading through irrelevant data or performing irrelevant data analysis.

Peter Baxter, managing director EMEA at Yellowfin, notes: “The inability to ask the right questions of data is often driven by a disconnection between data analysts and the business users or sales people, which can prohibit sales leaders from asking the right questions from sales data at the right time. A lack of collaborative capabilities and inability to combine sales data from one source with data from another are also common occurrences that prevent sales leaders from asking the right questions of their data.”

There are a number of things that sales leaders can do to ensure they have the necessary capabilities to explore their data and uncover the required insights, according to Baxter.

“Firstly, sales leaders should play a leading role in the purchase of the analytics platform that will be purchased to deliver those data-based insights,” he says. “This will help ensure that the solution can be delivered in a way that supports fast and easy independent data exploration – that there will be appropriate balance between the self-service needs of end-users (sales, in this instance) and the governance requirements of IT.

“Secondly, sales leaders need to collaborate with business analysts and IT to ensure that they are involved in the development of Business Intelligence content – such as reports and dashboards – to ensure that it meets their requirements. And, even before meeting with key content creators, sales leaders should carefully map-out how they envisage data analytics supporting specific business objectives to ensure that they are requesting the most appropriate information.”

No strategy

Data intelligence should give power to employees to become more competitive and service-oriented, and get closer to their customers. Buying the software is a good starting point, but without a strategy, sales analytics tools are ineffective.

“The first step is developing your strategy: what information are you collecting from your customers and how does that impact how you interact with them?” recommends Vera Loftis, managing director UK, at Bluewolf. “The insights you gain from your customers should not only inform that strategy but drive it. For example, if a company’s goal is to grow yearly revenue, are you simply going to tell your reps to sell more or are there customer insights that give a better view of who is likely to buy and when? Reps tend to spend more time on enterprises due to a bigger deal size, but your data you might find that medium size businesses are more likely to buy and in a quicker timeframe. This would indicate that the fastest way to drive revenue would be to try to capitalise on the mid-size market.

 KPIs are also a key component to success with sales analytics tools, so these need to be baked into a strategy. “What behaviours are you trying to instil in your team to accomplish your strategy and are those behaviours being adhered to?” continues Loftis. “In the previous example, the reps would need to transition from spending the majority of their time with large prospects to also making medium sized businesses a priority. With the captured activity data, companies can easily see trends in this behaviour through its analytics tool.

“Additionally, other important success factors include performance metrics, year-on-year revenue and hard facts. Are reps following the strategy we have laid out, is their behaviour reflecting that, and is that in return leading to the predicted results? Is our strategy impacting the bottom line?

“Time and time again companies make the mistake of missing out these first two steps, jumping to the third, and ultimately setting themselves up for failure. This is incredibly damaging for businesses because they are not reaping the rewards of data analytics which leads to disillusion of the technology, poor employee adoption, and wasted investment.”

Measuring the wrong thing

Setting vague KPIs that are not really measurable based on your sales analytics can be a recipe for disaster. The clearer you are on what you want to measure, the more likely you are to achieve the results you are looking for and have an impact on your bottom line. 

Loftis advises: “Don't try to measure everything! Dependent on the size of the company, you should have between 3 and 10 KPIs. The more accurate you are with what you want to measure, the better the results you will be able to harness from your sales analytics tool.”

Misunderstanding sales analytics

The single biggest reason that sales analytics projects fail to drive improvements is misunderstanding of what it is and what it takes for it to succeed.

“Too often businesses buy sales analytics solutions but actually use them as glorified sales reporting tools,” says Duncan Wood, marketing manager for Infor CRM. “Sales reporting answers the question “are we going to sell enough to hit our target this sales period, e.g. month or quarter?” Sales analytics answers the question: “how do we sell more products or services next month/quarter/year?” Reporting is retrospective. Analytics is forward-looking and requires human intervention to ask the question: “why?” and drill into that question until a premise has been established which can drive change.

“Looking forward includes an element of looking back, however in most situations, the prospective strategy requires “other” inputs as well: customer satisfaction, financial data, product quality improvements, etc. The opportunities to truly improve sales will involve more than fine tuning sales processes and more than likely involve other parts of the organisation too. The point is, the real customer journey and touchpoints are all important to improving sales in the medium to long-term.”

Critically, to increase sales, an organisation asking “how” must also be willing to make changes. Wood continues: “To give an analogy from a closely related field, whilst 98% of companies collect feedback from their customers, only 8% follow up with customers to do something about it.  Again, it is not just the measurement that is important, it is the interpretation of the results, what they mean to the organisation and critically what will change as a result. True sales analytics is the opportunity for organisational change to improve profitability, revenue and customer satisfaction through meaningful data driven insights across the customer journey. But that insight must lead to clear, well planned action that is understood by the key stakeholders in the organisation.”

The solution to this problem is to enter into a sales analytics project with eyes open, asking the right questions and have a way to create real (and execute) plans for organisational improvement. “The most important preparation is to form a cross-functional team with an executive sponsor,” advises Wood. “The team should also have the support of someone who truly understands the data and ideally has some background in statistics. The executive sponsor’s role is to help affect the changes the data/team think will improve sales. The results of the team may surprise you and some may be more obvious. The truth can be difficult to navigate so priorities for the objectives of the project are crucial.”

Focusing purely on sales figures

A mistake that is often made with sales analytics tools is solely focusing on sales figures. “Of course sales figures are all-important, but they are not the only metric for assessing sales staff performance,” warns Javier Peralta, UK country manager for ForceManager.

“By purely focusing on sales figures, organisations ignore the other factors that affect performance – how many calls sales reps make, how many emails they send, how many visits they make, what sales collateral they present to the customer and how many new prospects have been added. There may be sales reps who are doing all the right things but not getting the end results, so purely focusing on sales figures could damage the morale of those employees. Sales analytics isn’t just about the figures – it’s about the ‘why’.

“Managers and directors should also be looking at the volume of interactions that sales staff are having with clients and prospects – how many calls they’re making, how many emails they’re sending, how often they’re meeting contacts. By looking at these figures, users can identify staff who are working hard and those who need pushing to increase their sales, and then tailor support and training for those sales reps.”

Not sharing the findings

Some sales directors and managers collect sales analytics without sharing the information with their sales reps. The ultimate aim of using sales analytics tools is to improve staff performance and in turn boost sales results, and this is difficult to do if the data is not shared across the organisation.

Baxter notes: “Data is only valuable if it can be shared. Not too long ago, analytics tools were the domains of data experts within the organisation, such as the IT department. They used the tools to produce and analyse data but their resulting reports were difficult to interpret for non-technical business people. This was one of the reasons businesses tended to limit the delivery of BI based information to senior managers making strategic decisions.”

Nowadays, businesses recognise the value of fact-based, decision-making across the enterprise.

Ways that sales leaders can share their findings and bring them to life, according to Baxter, are:

  • Data storytelling: The ability to deliver engaging presentations by embedding live reports in a PowerPoint-like presentation and collaboration module.
  • Annotations: Annotating certain data points allows users to begin conversations about trends and overlaying human knowledge to pinpoint the underlying real-world events that gave rise to those trends.
  • Discussion threads: Enable users to converse about data and data analysis alongside the most up-to-date and accurate information available.
  • Voting and polling: Empower users to turn conversation into action by deciding on a collective course of action.
  • Automated alerts and scheduled reports.

Taking a ‘top down’ approach to the analytics project

“In the companies that I have worked with, the teams that saw the biggest initial challenges were those that only took a “top down” model when it came to developing their analytics,” notes Farnaz Erfan, director, product marketing and strategy, at Birst. “The main learning point here is that the whole sales function should be involved in the project from front-line sales people through to the sales leadership team and the management team as well.”

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