Traditional reporting typically focuses on describing what was achieved last month, or last year. This is important information also in the future, but in order to go further with knowledge-based management, decision-makers are interested in predictive analytics –ie. what is likely to happen next.
In the absence of a crystal ball, artificial intelligence is usually used to help. For example, Microsoft's Power BI reporting and analytics environment uses machine learning and statistical models to identify patterns in historical data and extrapolate them into the future. This enables a shift from passive monitoring to active planning, where decisions are made based on future trends.
Reliable forecasting requires much more solid groundwork than simply reporting figures. The most important prerequisite is sufficient quantity and quality of data. Algorithms need long-term historical data to learn to identify, for example, seasonal variations. For Power BI's forecasting models to reliably estimate the upcoming quarter's sales revenue based on marketing efforts, you often need at least two or three years of historical data to learn to distinguish random spikes from regular seasonal fluctuations.
The next requirement relates to data consistency. Fragmented data and conflicting data in different systems leads to misleading forecasts. This means that sales transactions recorded in CRM and cost data from digital marketing platforms must be linked to each other precisely, for example by campaign codes or customer segments. If the sales team records transactions with a delay and the marketing team measures success in terms of click-through rates rather than euros, AI has no logical cause and effect relationship to make a valid prediction of future performance.
Beyond the technical requirements, predictive analytics requires an understanding of the business environment. You need to know which factors will affect the outcome in order to feed AI with the right variables. For example, if you know that weather conditions or competitors' pricing will have a significant impact on your own sales online, you need to feed this information into the data model so that the predictive tool can take it into account in its calculations.
Once predictive models have been established, they enable entirely new ways to steer sales and marketing planning and day-to-day operations. If AI detects that customer attrition is increasing in a particular segment, marketing efforts can be targeted to these customers as a proactive measure. At the same time, budgeting becomes more dynamic: more funds can be allocated to the channels that are predicted to deliver the best results.
By combining marketing and sales data, predictive analytics can estimate which product categories will become more popular with specific customer groups during the next season. With this information, a company can adapt its offering, prepare to increase service capacity or replenish its stocks with the right products in advance. When predictive analytics is used, for example, to forecast demand, it is important that the same information is also used in other areas such as product and service development and inventory management.
Predictive analytics also enables a highly accurate and personalised customer experience. By analysing a customer's purchase history and comparing it with the purchase behaviour of thousands of similar customers, AI can predict which product a customer is most likely to want next. By presenting precisely the right solution on a website or in a newsletter to match the customer’s predicted need, the effectiveness of cross- and upselling can be considerably improved.
To realise the full potential of predictive analytics, organisations need to move from siloed working to close collaboration. In particular, sales and marketing must finally start to plan actions together and share their customer data. Both teams need to commit to maintaining data quality in real time. Sales must therefore record the results of customer meetings and update the stage of the buying process in their CRM system without delay, as AI needs this fresh feedback to understand which actions are predictive of sales.
Moving from reactive decision-making to a culture of experimentation that relies on predictions may be an equally demanding shift. Managers and experts must learn to trust the early signals from AI and have the courage to take action even if the predicted changes are not yet reflected in the execution reports. Closer collaboration between sales and marketing can take the form of planning meetings to jointly consider how to respond to the scenarios predicted by AI.
Like any AI output, predictive analytics is no substitute for human judgement. It only serves as a highly sophisticated decision support system. When guesswork and assumptions are removed, experts' time and energy can be focused on strategic planning.
Want to talk more about how your organisation can shift from passive data reporting to predictive analytics and change knowledge-based management from rhetoric to action? Contact us and we'll help.