How to use analytics to drive sales performance

One of the most significant ways that sales has evolved in recent years has been the movement to adopt a far more analytical approach, leveraging insights to drive sales performance. That’s not to say that there hasn’t always been an appetite to use as much information as possible to inform sales activity, of course. But a number of trends have combined in recent years to enable far greater insight into the sales process than has ever been possible before.

In his book The Power of Sales Analytics, Andris A Zoltners explains:

“As sales leaders ponder the challenges of structuring, sizing, deploying, hiring, developing, motivating, informing, and controlling their sales organisations, they are working with a new generation of technology-savvy workers and an explosion of data and technology that has several components:

• “Escalating volumes of information on customers, sales transactions, market potential, competitors, sales activity, and sales people.
• Social networks such as Facebook, LinkedIn, and Twitter.
• More powerful and fast-changing computer, storage, and mobile communication technologies.
• Advanced models and analytics tools.”   

Indeed, while reports within CRM can fail to deliver the insightful sales performance analysis that sales leaders desire, today’s sales analytics technologies – often a critical part of a sales performance management suite – can deliver the desired results.

A perfect response to the much more challenging sales environment that businesses find themselves in today, these sales analytics tools can enable sales leaders to answer some of their most important questions:

• Which deals are most likely to close?
• Which sales rep has the best chance of closing this opportunity?
• What is my predicted close rate on my pipeline?
• Why do I win /why do I lose deals?
• What is the next best action for this opportunity?

Sales analytics, when not used in isolation, are said to have the power to transform sales performance. According to Sirius Decisions, sales analytics “enable more effective, fact-based decision-making”, and sales leaders should “leverage analytics to complement their judgment and experience”. These analytics give sales teams more of an understanding of their pipeline and funnel, and a better idea of what’s going on in certain stages: when deals will close, how long they’ll take, and more.

The upshot is that sales analytics are emerging as a core differentiator for top performing organisations. The 2015 State of Sales report states that high-performing sales teams are three times more likely to be using sales analytics to drive sales than those under-performing teams.

Little wonder, then, that investment in sales analytics tools is rising rapidly, and according to the 2015 State of Sales report, there will be a 58% increase in planned sales analytics use from 2015 to 2016.

So how can sales leaders sweat the most value out of their sales analytics investment?

1. Ensure your data is complete and accurate

Any sales analytics tool will only be as effective as the data entered into it from sales force automation and/or enterprise resource planning systems. But many companies tend to employ such tools in a rather undisciplined way, failing to provide sales staff with simple, structured templates laying out the data they want to collect.

It is worth focusing time and effort on ensuring that the quality of your data is high by undertaking a data cleansing project on existing information and making certain that it as complete and accurate as possible.

This is an ongoing process, and it is crucial to ensure that sales staff accurately input their data and keep their records updated with pre-defined information such as potential opportunities at each stage of the sales cycle. To encourage them to do so, make it clear that the organisation now relies on such information, which means that it is considered a core responsibility to accurately record their activities in order to ensure that the team, and the company itself, performs better as a whole.

2. Take your sales analytics initiative one step at a time

Sales analytics tools can help you track and understand the behaviour of your sales force and also give you insights into why customers buy what they buy.

But there are different types of tools on the market to help you do this and Gartner splits them into four categories, each of which is on a continuum:

• Descriptive – What happened? For example, which key opportunities did we win or lose?
• Diagnostic – Why did it happen? For instance, we won or lost due to competitive pricing.
• Predictive – What will happen? For example, win rates are likely to improve next quarter.
• Prescriptive – How can we make it happen? For instance, we will improve win rates due to more effective value selling.

This means that it is usual to start your project at the descriptive and diagnostic levels and, once experience has been gained, to move onto the next stage.

3. Understand what outputs will help you attain your objectives

In order to make the most of your investment, it is important to have a clear view of what you are trying to achieve – so what are you using the tool for and to what end.

To help things along here, consider which part of your sales process needs attention, either because it is poorly-understood or broken. Then, come up with very specific questions about that area in order to gain a clearer idea of what is going on. Such questions could include how long it takes on average to close a deal and which customers buy what products where.

4. Ask 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.”

5. Ensure you have sales operations expertise in place

If a given sales manager does not have sales operations expertise, it may be helpful to bring such skills in. Sales operations personnel are logistical, process-oriented people who provide discipline and structure when working with sales analytics tools. Many are data scientists, or are in the process of becoming one, and undertake activities such as preparing reports, evaluating sales enablement tools and setting up systems.

6. Keep things simple and iterative

Keeping things simple and limiting the initial scope of initiatives generally works well. The old adage ‘deploy early and deploy often’ applies here, with each stage of the process comprising activities that are linked to key deliverables, which are made available at the end of each phase.

This means that new features are rolled out iteratively in chunks on a must-have basis, which in turn enables you to understand what has been used and what works. Managing this process is likely to require an experienced project manager or subject matter expert in order to keep nice-to-haves to a minimum, however.

Over time, data from other departments such as marketing and customer service can also be added in order to encompass the entire business, but the key lesson here is that it pays to start small.

7. Don’t focus 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.”

8. Share 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.