One Secret? There are at Least Three Pressing Reasons Why Predictive Sales Analytics Cannot Wait Until Tomorrow
Predictive analytics covers a variety of statistical algorithms from machine learning, predictive modelling and data mining. It analyses past facts and historical data to make predictions about events and to identify the likelihood of future outcomes. Predictive analytics has been around for years, but it was impractical in large-scale sales settings due to an inadequate data analysis infrastructure.
However, predictive analytics has lately taken a more prominent role as a viable technology for B2B companies looking for projects with high ROI (Return on Investment). As access to more sales data has increased (ERP, CRM, Social Media, eCommerce, etc.), new software tools and powerful hardware have been developed, predictive analytics has become unavoidable in B2B Sales. The key question is when an organisation is beginning to take advantage of this technology.
In this article, we present general managers and sales leaders with three good reasons why predictive analytics cannot wait until tomorrow. First, by not using predictive analytics, a company is most likely already losing money, for predictive analytics can optimise prices and reduce costs. Second, since predictive analytics is nowadays top of the agenda for many organisations, one should assume that a competitor is already using it. Third, by not implementing predictive analytics, companies are risking market share, customer satisfaction and sales productivity.
Reason One: If your company is not using predictive analytics is probably losing money.
Not using sales analytics means not being able to assess the current performance of a sales team. This is bad. Nevertheless, not using predictive analytics means ignoring the future. This is very bad.
There are several applications for predictive analytics in B2B sales that can actively influence the future financial results of any company, including price optimisation, churn reduction and lead scoring.
It is a well-known and interesting fact in B2B sales, that, from time to time, the price of some products might go rogue. Maybe a Key Account Manager (KAM) is trying to close an attractive deal; perhaps a customer has accumulated several discounts. Prices go down; margins go red. Unfortunately, most sales leaders only realise about this when it is already too late.
If you use predictive analytics for price optimisation, you do not need to wait until your finance director angrily calls you. Predictive analytics can point out product or customers most likely to rip off your company – before it happens.
If not using predictive analytics, a company is most likely already losing money.
Churn modelling is one of the hottest application of predictive analytics. Managers identify customer retention as a top goal in most B2B organisations. However, effective retention incentives, for example, discounts or special promotions, can be expensive.
Why giving resources away to all customers, when a company can target only those most likely to churn? The monetary benefits of customer loyalty measures come from targeting just the clients with the highest risk of defecting. Predictive analytics helps to classify them and so to save money.
Lead scoring or qualifying is another example of predictive analytics moving the needle in B2B. Companies usually track hundreds of open deals or opportunities. Effectively following up on all of them is impossible. How does a KAM decide whether a new lead offers a real sales opportunity? Predictive analytics finds those sales leads most likely to turn into an actual sale. Why waste time and money on prospects that will never buy? Why not visit potential customers instead, to timely close a real deal? Here as well, predictive analytics is the answer.
In summary, predictive analytics offers several interesting cases with high ROI for B2B sales. Among companies who have implemented it, two-thirds declared that predictive analytics provides ‘very high’ or ‘high’ business value. One recent study (see below) showed that predictive analytics projects return a median ROI of 145 %. In comparison, non-predictive business intelligence initiatives’ median ROI is 89 %.
Reason Two: Your Competitor Is Probably Already Using Predictive Analytics
The use of predictive analytics offers a critical competitive advantage in Business-to-Business. Since three in four enterprises are already investing in analytics, your competitor is probably implementing one or more cases of predictive analytics by now.
Sales in B2B are always a race. Your customers cannot wait, and your competitors are always on. If they can use software or a tool to gain a competitive advantage, they surely will. If your business doesn’t use predictive analytics, it’s losing out – big time.
Think of retail, where predictive analytics is set to rise in importance, helped by newer sources of data and large-scale correlational methods. By using predictive analytics, the gross margin dollar per Stock Keeping Unit (SKU) can be increased by 40 cents. What would you do, if you could gain an extra 40 cents per each dollar of stocked product in your inventory? Hire more Key Account Managers? Increase your investment in marketing? Open new markets? Venture with technical innovations?
Do you believe we are overstating the danger? With just a fifth of its revenues, Amazon doubles today the market capitalisation of Walmart.
For example, at the time of writing, Facebook and Amazon have a higher market capitalisation than IBM and Walmart because they were pioneers in the use of predictive sales analytics. Of course, Walmart and IBM can afford to compensate for the misjudgement and are closing out, but they lost the predictive analytics race. If your business is not applying it, it will lose against its competitors as well.
Another example? It took Disney another 9 % fall in year-on-year profits and stagnated revenues in 2016 to understand the power of data. The company then announced that it would start self-distributing its content, bypassing Netflix. Netflix introduced online streaming in 2007. The customer could now watch movies and TV shows on their computer. This gave Netflix a massive competitive advantage, convenience, and an enormous amount of data. Ten years after, Netflix possesses the data to make the most informed business decisions, while Disney does not.
Add to the race the limited number of data analysts. While many companies are already building data analysis capabilities, the number of data scientist and analysts remains almost stable. In other words, many enterprises are competing in another front, one about human capital and expertise. This is a definite reason why your company cannot wait until tomorrow to get started with predictive analytics. If it does, most likely it will cost you dearly. Like it did to Walmart and IBM.
Reason Three: How Risky Is Not To Use Predictive Analytics?
Not using predictive analytics is risky. What an enterprise gains with predictive analytics is, in fact, an effective way to decrease business risk. A general manager could think of the biggest risk facing her company. Predictive analytics can surely help her to reduce it. Is it customers not buying again? Is it customer satisfaction? Is it employee retention? Any of these examples are today a cost-effective application of predictive analytics.
Why then are not all enterprises hiring data analysts like there was no tomorrow or implementing predictive analytics? Because, in general terms, business risk is hard to evaluate. Risk assessment is problematic for several reasons: management bias, resistance to change and subjectivity, lack of consistent information to determine risk, judgements based on personal impressions and opinions rather than real facts, among others.
At the time when predictive analytics is becoming mainstream in B2B sales, competitors are most likely using it, and its ROI is increasing, the risk of not adopting this technology grows exponentially. Imagine again that your competitors have a secret tool. This tool tells them in advance which of your customers might be looking for alternatives, where your product’s weaknesses are, or which of your buyers’ segments could buy more. How well will your sleep?
Do you believe that we are exaggerating the danger? Just look at retail again. In a little more than a decade, Amazon became king where Walmart once was. At the moment of writing, with just a fifth of its revenues, Amazon doubles the market capitalisation of Walmart. Why? Mainly because of applying data analysis and predictive analytics to massive amounts of data. Can Walmart fight back? Somehow, although by partnering with, for example, Google.
What would you do, if you could gain an extra 40 cents per each dollar of stocked product in your inventory?
We can say the same of Facebook and Google, who have dethroned established leaders in the media market, by using data analysis and predictive analytics for advertisement and marketing. They created whole new markets and went on to dominate them. They make customers happy and sell with a degree of efficiency only dreamed of twenty years ago.
Retail, distribution and media are not the only industries that predictive analytics is transforming. Eric Siegel, the founder of the Predictive Analytics World Conference, listed marketing, financial services, credit risk analysis, insurance, workforce management, healthcare, manufacturing and government. B2B sales are no exception. Not using predictive analytics is nowadays a risky business.
Three Reasons Why Predictive Sales Analytics Cannot Wait Until Tomorrow – Conclusion.
Many applications of predictive analytics were commercially unappealing years ago. Nonetheless, recently, due to improvements in infrastructure and increased amounts of data, predictive analytics have become a must-have in B2B sales.
In established industries, companies lag with the adoption of new technologies. This, however, does not appear to be the case with predictive analytics in B2B sales, at least in some countries and business areas.
In this article, we have argued why managers should immediately adopt predictive sales analytics. The key point is this: predictive sales analytics is mature enough to offer secure ROIs, young enough to differentiate your company from your competitor.
There are first the attractive financial benefits of it. Predictive analytics has demonstrated revenue growth and cost reduction over many business units, including sales and marketing.
Due to its high ROI, predictive analytics offers an “unfair” competitive advantage. Some companies should guess what to sell and to whom, while others let data mining predict their best actions and prospects.
Besides, while the amount of data increases exponentially, the number of data scientist available grows only slowly.
In turn, the incredibly compelling ROI of predictive analytics makes taking no action a risky path. Companies not implementing it are jeopardising their sales productivity, customer satisfaction and market share.
When is your company starting with predictive analytics? Do you have any further questions on this topic? We are happy to help!