Four steps to improve your sales operations with predictive analytics
Optimize your sales planning with Predictive Analytics in four steps.
Sales and Operations Planning (S&OP) encompasses two close-related fundamental processes: sales planning and operations planning.
S&OP is perhaps the most crucial planning process of any company. The primary goal of S&OP is to align three critical but opposing goals: sales productivity, customer satisfaction and inventory optimisation.
With the help of predictive analytics, sales leaders can nowadays improve operations thanks to increases in sales productivity.
Predictive analytics reduces the time sales teams spent in unproductive non-customer-facing activities. Furthermore, it also provides a decisive competitive advantage in highly competitive industries.
There are four necessary steps to improve sales operations with the help of predictive analytics. First, you need to simplify your sales operations. Second, apply the predictive analytics models that will impact your earnings highest.
Third, use the sales data that your company possess. Four, act on the insights you gain and make the operation action-oriented. Let’s review these four suggestions in detail.
Step one: Simplify your sales operations
Simplicity is probably the most disregarded success factor in B2B sales. Having a complex S&OP hinders the gathering of valuable and actionable sales data and the application of predictive analytics.
On the other hand, what is simple is measurable, comparable and actionable. An unassuming S&OP is easy to analyse and to predict. A simple S&OP will add value to your sales organisation. It services usage from your salesforce. Sales and planning teams follow simple processes better.
Salespeople love simplicity. For example, if you simplify your sales planning process, adoption from your sales team will increase. At the same time, the sales team’s commitment to predictive analytics will increase. They will be able to understand and apply its recommendations.
Simplicity means the management will have to compromise. For example, if you are tracking open sales deals, every Key Account Manager would like to see different characteristics on your sales planning software, such as deal status, type of products, deal size, decision-makers involved, reasons for not buying, or the likelihood of closing. The list of wishes can have no end.
What should a sales leader do? Sales managers should simplify sales operations to the bare minimum. They should, for instance, adopt as many steps in their sales planning process as needed; as few as possible.
Simplifying sales operations enables not only actionable predictive analytics, but it also accelerates sales and makes customers happier. Review your S&OP and make it simple.
Simplicity is one of the essential principles in any planning process. It is critical if you want your S&OP to profit from predictive analytics.
Predictive analytics reduces the time sales teams spent in unproductive non-customer-facing activities.
Step two: select the predictive analytics methods with the highest impact on earnings.
The fundamental goal of S&OP is to and improve sales productivity, increase customer satisfaction and optimise inventory. Optimizing sales productivity, customer satisfaction and capital will, in turn, affect company earnings.
Therefore, a company should select the predictive analytics methods that will impact these mentioned three KPI the most. There are no one-size fits all, but there are clear guidelines on which predictive analytics methods to implement.
First, here simplicity is again of the essence. If a company uses dozens of KPIs to control its S&OP, forecasting them will add little value. Management should instead strike the right balance between what it is possible and what is desirable.
Discuss with your sales team what are the KPI with higher influence on your productivity, stock levels and customer satisfaction. It can be anything from pricing, customer attrition, available cross-selling opportunities or stock forecasting.
Predicting a limited set of critical KPIs with well-known methodology enables cost-efficient forecasting. It also shortens implementation and reduces investments.
Also, as a general manager, monetise in advance improvements in each KPI. Furthermore, do not start a predictive analytics project without a draft on the ROI (return on investment) you can expect from it.
Step three: Use the data that you have before investing in external data or data quality.
One common mistake when thinking about predictive analytics is the falling for the false assumption that your company does not possess enough data, good quality data or “the right” data – whatever the last one means.
Yes, you might need external sources of data to improve your sales planning process using predictive analytics or the quality of your current data might be wrong. However, do not rush to buy these two assumptions before using first the sales data that you have. Most companies have ERP and CRM data – start there.
You should consider whether external data sources are needed to improve your sales operations and the quality of your existing CRM and ERP data. Also, since predictive analytics is a close relative of data mining, later along the line, you will discover precisely what data you could be missing or where to improve the quality of your existing data.
Companies can enrich CRM data with, for example, acquired B2B databases. Furthermore, they can check and improve their existing ERP data vs external records. In many cases, small improvements in data quality can lead to significant improvements in predictions or can expand the scope of predictive analytics software.
Start improving applying predictive analytics first on your S&OP using the internal data that you have. Once you have obtained experience, consider expanding your databases and improving quality.
Predictive Analytics provides a decisive competitive advantage in highly competitive industries.
Step four: Make your sales operations action-oriented
Applying predictive analytics methods to your S&OP if it is not action-oriented produces no value. What does it mean an action-oriented sales planning process? It means fundamentally two things.
First, S&OP should include practical action to deal with predictions, forecast, alarms, and similar. Second, it means that your sales team should be willing and capable of taking these concrete measures timely.
If a customer is about to churn, not paying profitable pricing levels or there are good chances for cross-selling, your sales team should do something about it.
If predictive analytics points you to possible future problems, your sales team cannot passively wait for them to go away. It must be willing to take actions that will ensure the success of your company’s S&OP.
Having an action-oriented S&OP is harder than it sounds. Experienced managers know by heart the challenge of mixing what is essential with what it is urgent.
Leadership means making difficult decisions, prioritising, taking calculated risks and using new opportunities. Clear guidance will lead to swift action.
In the end, only action-oriented S&OP profit from predictive analytics, for planning is always a contingency. Plans themselves are not as important as the planning processes. To be ready for action, to prioritise using predictive analytics, if necessary to change course, differentiates between good and great strategic and operational sales planning processes.
Four steps to improve your sales operations with predictive analytics – Conclusion.
Sales and Operations Planning is perhaps the most critical planning process of a company. S&OP aims to align three conflicting goals: sales productivity, customer satisfaction and capital usage.
Applying predictive analytics is today necessary to improve B2B sales productivity. Predictive Analytics provides a decisive competitive advantage in highly competitive industries.
In this article, we discussed four necessary steps to improve sales operations using predictive analytics.
First, a company should consider simplifying sales operations. Simplicity leads to adoption, measurability and the affordable application of predictive analytics.
Second, predictive analytics should be used to improve earnings. Therefore, companies should apply models and methods that impact the earnings the highest.
Third, before considering acquiring external data sources or investing in data quality, companies should use the sales data they already have. No dataset is perfect, and most can deliver good returns.
Lastly, the whole sales team should be ready to act on the insights gained using predictive analytics. Companies should define and reward specific those actions.
Professional Lead Management: Step by step to new customers: An agile journey through marketing, sales and IT (in German)Markus Paatsch. “Conception and development of an information system to support sales planning in solution sales.” Karlsruhe Institute of Technology. (in German)