Predictive Sales Analytics: Can you anticipate your customers’ journey?
Nowadays, new technologies enable a highly detailed understanding of the way customers buy, fancily known as “digital customer journey”. They also allow a cost-effective application of predictive analytics in Business-to-Business (B2B) sales.
However, mapping this customer journey represents – at the same time – one of the most difficult challenges companies face. As customers increasingly move their buying operations to digital channels, including social media, internet and e-procurement, it is becoming harder to understand how buyers are indeed buying.
Companies can follow a three-steps recipe to map these journeys efficiently, understand how buyers buy and anticipate customer needs: analyse sales data, define actionable customer segments and align sales and marketing behind sales predictions.
Customer Metrics and Sales KPIs Really Matter
In the last couple of years, sales in B2B have been facing a shifting ground. Customer-directed journeys are replacing the traditional sales funnel or one-dimensional pipeline approach.
In this new B2B world, sales data is gold. This information can come in many forms and from many sources: ERP Sales Data, CRM Data, Social Media, Website interactions, the company’s own products (IoT) and other relevant data points. Sales Analytics and data visualisation make these datasets understandable.
Predictive Analytics creates the most value out of them.
Digital B2B sales is a new field for many organisations for three key reasons: companies need to embrace sales analytics, refocus resources and bring sales and marketing together. Knowing what, how and when customers buy can have an extreme impact on sales growth and efficiency.
First, companies should understand their customers. To digitally perform this task, they need to analyse all the past interactions and transactions with existing and potential buyers. Therefore, customer metrics and sales key performance indicators (KPI) are extremely relevant. Selecting the right set of KPI and customers metrics is critical to understand, measure and predict the customer journey.
How to select the best KPIs and metrics is still more a craft than a science. In this context, customer metrics should reflect every different buying behaviour, while sales KPIs should focus on the actions that the B2B sales team can undertake to manage and influence these journeys.
There are lagging, leading and advanced customer metrics and sales KPIs to consider. Examples of customer metrics are awareness, website visits, content accessed, clicks, product usage, customer lifetime, etc.
Examples of sales KPIs are average deal size, the number of sales activities, win-rates, Loyalty Scoring or Churn-Risk, Unfulfilled Sales Potential and Pricing Opportunities among others.
The question is: how deep should a company dig into each customer and how many KPIs are too many? The usage of sales data enables B2B sales teams to map all digital customer interactions leading to a deal.
Can’t see the wood for the trees? Map journeys by customer segment
With the sales data, customer metrics and sales KPIs in hand, sales leaders gain an advantage by mapping customers journeys as detailed as possible, but no more.
All clients buy differently. These days business-to-business purchasing decisions increasingly dash difficult journeys. However, it is not a sensible idea to map them all. This new situation challenges, in many cases, the long-standing practices of successful sales organisations. Segmentation is the key.
First, sales managers should identify customer segments and chart the journey its decision makers undertake. Big organizations with thousands of customers will surely need data-driven research (by mining ERP data, for example) to get deeper insights. Sales managers can then split this down into at least three main sets of buying activities.
Second, sales leaders should analyse and cluster customers in each target segment, based on the journey they make.
Beware at this point that segmenting customer journey is not about using existing customer segments, such as customer size, location, industry or any other past criteria. Segmenting digital customer journeys is about clustering customers using the different types of buying behaviours. Only by segmenting customers based on the way they buy can a company successfully anticipate which sales actions or information are needed in each step.
Third, and crucially, sales managers should adapt sales processes to data-driven science. That is, they should design and implement a set of activities that correspond to each cluster of customers. The effectiveness of these changes over time should also be measured.
One step at the time: from data mining to predictive analytics.
Mining sales data and customer journeys represents a mean to an end. The real goal of mining sales data is to be able to reallocate sales resources quickly and to focus on opportunities with higher predicted likelihood of closing.
If the data analysis shows that a sales organisation can convert potential leads faster or with greater benefits, these opportunities should be given priority. The whole idea of predictive analytics is to embrace data analysis as an opportunity, to understand customer behaviour and to respond accordingly.
For example, in many cases, visiting or cold-calling a prospect does not modify a customer buying pattern. In comparison, a possible buyer that has just download a withe-paper or filled out a complaint might need urgent attention. Where should a company invest its resources first? Businesses can estimate the relevance of each type of customer interaction by building a decision tree and calculating their information gain. In simple words, companies can derive from their sales data, what kind of interaction maximises the chances of closing a deal.
It is important to note that this type of data-driven sales behaviour involves a joint commitment between sales and marketing departments. If a key account manager’s time is invested in specific segments, customers or opportunities, a company will do better by supporting her with marketing (or inside sales) activities.
Following predictive insights does not mean not paying attention to long-term sales relationships. Predictive sales analytics means reallocating the most expensive resources to areas of higher productivity. Maintaining an existing customer relationship can wait one week. Avoiding a customer from churning to the competitor or helping him place an order at the end of the quarter cannot.
As B2B executives in marketing and sales organisations push ahead with these changes, they will also need to reach across their enterprises and sharpen the customer focus in every business function. It makes very little sense to run after high-potential sales deals if the entire company does not work together.
Predictive Sales Analytics and customer digital journeys in B2B – Conclusion
B2B sales have never been more exciting. Sales leaders can count today on several valuable data sources: ERP Systems, CRM Solutions, Digital Marketing, etc. Several companies are now harvesting data from the usage of its own products (IoT – Internet of Things).
The usage of this sales data enables B2B sales teams to map all digital customer interactions leading to a deal. Do you know the song, digitalisation in B2B sales? This is what it sings about.
Moreover, they can anticipate customers’ needs using predictive analytics and react in a manner that ensures success.
Analysing customer journeys offers a great chance to grow revenues, avoid customer churn and increase customer satisfaction.
Companies should first define and measure the customer metrics and sales KPIs more relevant to the kind of customer journey they are analysing. Afterwards, they should identify customer segments based on those journeys.
Finally, companies can only gain if they use these customer journey maps to undertake the most promising sales actions, focus on deals with higher likelihoods of closing and to align marketing and sales.