Analysing sales data at the most basic level is quite simple – “how much of our product have we sold and who gets the recognition?” This evaluation should provide information on what was sold and by whom.
From this, businesses can set the next set of goals, how this is turned into next year’s budget, targets are allocated and so on. However, the current economic climate is making this level of planning more difficult, and leading business managers to ask more difficult questions.
These can include the following:
- Do we know why and how our products were sold?
- Is this repeatable?
- Can we reduce the time it took to sell?
- Can we increase the deal size?
- Do we know what has changed since we last looked at sales performance?
Asking these questions is important. The number of ways that organisations interact with their customers has increased, so now the potential for improving sales efficiency through analytics has become paramount to compete. The big mistake we can make is to repeat the ‘Big Brother attitude around analytics that existed in the past.
Big Brother analytics states that:
- You must enter all the data we ask for and we the management team will analyse your performance.
- Customer and sales analytics is for management use alone, not for everyone.
Sales people in particular are fiercely independent and Big Brother analytics fights against this independence, instead of embracing it.
Now, significant changes around data gathering and analysis have occurred. The amount of data available to companies is exploding: consumers are prepared to tell you more about themselves, the use of radio frequency identification (RFID) in the supply chain and retail makes tracking demand and movement for product easier, while the availability of smartphones and CRM systems has made data input simpler as well.
With this huge amount of data on tap, sales organisations need to look again at how they analyse their performance. This will provide more evidence on how results are achieved and whether there is a smarter approach for the future. This begs the question, are we moving from Big Brother sales management to Big Data sales management?
Big Brother says: Sell more!
Lets take an example. Company X has both direct and indirect channels to market, and wants to increase its revenues next year. What options does the company have?
- Create more products to sell
- Acquire more customers
- Sell more product to existing customers
- Hire more sales people
- Leverage more partnerships
- Create more demand through marketing
- Increase the price point for current products
A lot of organisations are very product focused so it may seem natural to create more products to sell. Some are first and foremost more partner-centric and so they will plan to expand revenues by creating new partnership opportunities. Others are more customer-centric and think to expand by better understanding customer buying behaviour. Some may simply be able to double the price of the current product line-up without losing their customers.
The reality is that a company will evaluate and try to do a mixture of these activities based on its own strengths and the position of the overall market. Given the current economic situation, most companies are choosing defensive positions. In this example, Company X could opt to focus on generating more revenue from existing customers alongside elements of options 2, 4, 5 and 6 above.
The challenge to overcome in this scenario is to know who the customer is. Company X therefore gets the IT department to build some customer reports and add in partner, product and sales rep data. Following this, lists are created and a ‘buy other things from us’ campaign is run. Shortly after, another more targeted campaign to customers who are interested and business goes on.
While this is great in theory, there are a couple of potential problems with this approach. The first is getting the data in the first place. This can be tough as it is often held in separate systems and requires consolidation. IT needs to be involved, taking more time to deliver the answers.
The second challenge is that this approach is a centrally driven and isolated instance of looking at the data. With this “Big Brother knows best” approach, adoption and belief at the sales level is low. This leads to the management team scratching their heads and wonder why the campaign was not a huge success.
The reason for this is that sales did not drive the process as a regular analytic review of their activities; instead, the emphasis is on Big Brother to give them another campaign and provide results for follow-up.
Big Data says: Analyse and predict more…
A smarter approach to this is to use existing data in a smarter way. There is a lot of hype surrounding Big Data and use of analytics but the truth is that there are real positive results that can be delivered by looking at data in a more efficient way.
The US presidential election campaigns saw Big Data analytics being used as part of voter engagement and turn-out modelling. As both candidates looked to sell themselves as the right package to meet the needs of the country, analytics techniques were used to see the impact of marketing tactics, press activities and other events.
What we really learn from the US presidential elections is that Big Data alone is not enough to win a campaign. Mitt Romney’s campaign team had access to Big Data but the campaign did not execute as well as it should. Barack Obama’s team ran their Big Data analysis every night to work out the likelihood of winning and used this learning to adapt and change their activities daily.
From a sales perspective, Company X could kick off a Big Data project to get to know its customers better. This would involve spending months integrating data from every possible interaction with customers. Social media and Twitter feeds, CRM application data, web site analytics and enterprise resource planning (ERP) data can be combined to work out the ideal predictive model for every customer interaction. This would tell the company what the likelihood of customer churn is as well as their propensity to purchase. This can then be packaged and pushed out to the sales and marketing teams, with a lot more specificity than the Big Brother campaign.
However, it ignores some of the areas where data cannot be captured, such as personal interaction. The challenge with relying on data and removing the ‘dark art’ of selling from the equation is that there are a lot of people and relationships involved in any sale. Purely relying on data to tell you what to do will not work. Sales has to drive the analytics in order to believe and trust in the data for them to embrace and act on the insights. Ideally these insights should be seen to support their own ideas rather than being perceived as a data scientist telling sales how and what to sell.
There is also a significant potential for delay to affect getting these insights together. With ever more data being created, these projects can be huge undertakings that can struggle to keep up to date with customer demands.
Big Opportunity says: empower your sales organisation with analytics big or small.
These two previous alternatives represent the far ends of the spectrum when it comes to analytics. To really improve sales performance through use of analytics should very simple. The most important thing to consider is that everyone needs to be involved, the entire customer experience needs to be considered and you should start tackling the small problems first. By focusing on the basics first, this gives sales the opportunity to get started and take ownership of the analytics.
One simple way to get this going is to create peer-to-peer sales performance dashboards. This enables the sales teams to compare performance at a true like for like level. This involves not only creating and measuring standard metrics like percentages of quota but also looking at market penetration data and customer potential metrics.
When this data is exposed, the emphasis should be on stopping arguments about comparisons and instead focusing on how to improve metrics overall. Once the sales teams have an environment where they can measure their performance, they then want to understand how other sales people are being successful. Are other teams running different campaigns or making more calls? Are their sales cycles shorter than ours?
Once sales professionals can look at their own performance in context, it encourages them to consider how they can use data in a more effective way. This trigger moment makes sales more efficient in how they approach activities, which has the effect of making the organisation more customer and data-centric as a whole. Another benefit of this is that forecasting improves, helping to generate smarter revenues.
The next step in this evolution is to feed the sales organisation with ‘what-if’ scenarios and predictive analytics. By putting this kind of information into the same environment that is measuring sales performance, it encourages sales people to look at what they are doing individually and as a team. Once they understand how to use data in this way, there is usually huge demand from the rest of the sales team as they can see the value that is created. It also encourages more proactive monitoring of results, as they can see direct feedback.
Organisations can also look at how they can enable partner and customers to access these analytics too where it makes sense. This has the effect of vastly improving business productivity with partners, through similar peer-to-peer analytics. For customers, having this data can be a really effective way for them to plan more of their own activities, which can in turn drive more sales for the company.
Data is at the heart of how businesses can improve their performance going forward. However, taking the right approach to analysing and understanding that data is a necessary next step. This big opportunity needs the sales organisation to drive it rather than have it pushed to them. Those that do embrace it will be the ones to succeed.
About the author: Jason Bissell is vice-president of international markets at Birst. In this role, Bissell is responsible for growing Birst’s EMEA and APAC operations as the company seeks to help customers gain greater value from their business intelligence (BI) and analytics projects.