Effective use of data can bring major benefits to businesses, but to extract the real value from all the data you hold in-house you’re going to need some serious analytics tools. These kinds of tools can be used to drive dynamic pricing strategies, personalized marketing and advertising campaigns, special offers or discounts, sales promotions and product recommendation engines.

In fact, a recent study found that around two-thirds of CPGs use data analytics to help forecast demand and around half use it for consumer insights. These companies also reported using data analytics for inventory and assortment planning, and marketing mix optimization. But, it’s not all plain sailing here. Analytics can be a challenging area, especially given the huge amounts of available data and the speed at which new information is created. Many executives report feeling pressure to obtain results more quickly, to react faster and derive deeper and more actionable insights - but less than a third report feeling confident in their ability to do this.

Some of the key barriers to effective use of analytics include a lack of strong data skills across organizations, a lack of access to understandable tools, and a culture that traditionally places ownership of data and analytics in the hands of a few specialists. So, let’s try to change that, shall we? For starters, to gain a good understanding of the analytics tools on offer, let’s take things back to basics and look at the four different types of analytics…

Descriptive analytics is the most basic form of analytics. It shows brands' historical data and attempts to answer the question: “what happened?” It’s generally focused on numbers and trends, like levels of website or app traffic, sales counts for top-performing products or services, or how many people responded to a recent advertising campaign.

Diagnostic analytics takes descriptive analytics a step further and attempts to address the question: “why did this happen?” It digs deeper into the data and seeks to discover why certain events took place. It will do things like identify anomalies, unusual patterns or sudden changes in levels of activity, before looking at the data and external factors for context and possible explanations.

Predictive analytics combines historical data with current trends to answer the question “what might happen in the future?” It doesn’t state exactly what will happen, but rather, what might happen in certain circumstances based on previous evidence.

Finally, prescriptive analytics addresses the question of “what should we do next?” It looks at all of the available factors in a given situation and, with the help of AI, makes actionable recommendations on what you should be doing to achieve better results.

Using & Analyzing Data

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