Organizations have the opportunity to grow by becoming more customer centric aligning their operating model around the customer to drive an effective market strategy. A key step in designing a customer centric operating model is to analyze and predict a customers future buying behavior.
The business intelligence market is growing nine percent per year and will exceed $80 billion by 2014, with approximately 50 percent ($40 billion) from predictive analytics (Gartner).
Predictive analytics is an area of statistical analysis that deals with extracting information from data and using it to predict future trends and behavior. It encompasses a variety of techniques from statistics, modeling, machine learning, and data mining that analyze current and historical data to make reliable predictions about the future e.g. likelihood of a marketing campaign succeeding and the revenue and profit uplift to be foreseen. Put simply it enables you to take action ahead of the curve.
With the right software tools it is now possible to collect, analyze, and mine massive amounts of structured and unstructured data for new insights. Exploring big data and using predictive analytics is within reach of more organizations than ever before due to technological advances in computer hardware and new technologies.
There are critics around the certainty of knowing how someone will behave in the future based on past behavior. We know from research that there is not a straightforward correlation between behavioral intent and subsequent attitude and behavior. Environment invariably influences people and trying to predict what people will buy next assumes that all the variables can be known and measured accurately.
Predictive analytics nevertheless remains a hot emerging topic and should be part of an organizations customer strategy. There are many predictive analytics software tools available. Many vendors are developing or have a predictive analytics solution from the large software players, to small start-ups emerging out of stealth mode. Eric Siegel in his new book Predictive Analytics the power to predict who will click, buy, lie, or die uses real-life examples to illustrate well how predictive analytics unleashes the power of data.
Predictive analytics is typically a four step-step process:
- Establish objectives Establish what you want to achieve, develop hypothesis with experts and the data required.
- Collect good quality data – Establish a view of customer combining enterprise and social media opinions, intent and sentiment including unstructured and free form data such as comments, emails, tweets, Facebook posts and SMS messages.
- Understand behavior and intent – Understand customers behavior and intent across channels and platforms by deploying predictive analytics in conjunction with organizational wisdom.
- Predict action – Predict a customers next purchase and make the right offer at the right time and in the right way. Evaluate and adjust as required.
Who can use predictive analytics tools is also changing. Historically it required advanced skills resting firmly in the domain of highly specialized mathematicians and statisticians. However, a shift is happening now towards the typical business user as new predictive analytics tools are becoming more intuitive, easy to use and a non-experienced user can interpret and act on the findings. Business users are looking to become more empowered and say they want tools they can access independently. Marketing, sales, finance and other roles now find predictive analytics are playing an increasing part of their role, and so are key players, influencers and indeed purchasers of predictive analytics solutions.
Furthermore, the target buyer for this type of offering isn’t just in the enterprise space it’s also expanding into the midmarket and directly to end users. Many vendors have or are establishing products, which are attractive to these two buyers.
Predictive analytics has emerged as a key segment in the big data market. It is clear it holds significant potential for organizations across all industries and sectors when combined with insight and expertise. Usage over the next months and years is likely to significantly broaden. The key challenge for companies starting on a predictive analytics path is to set out a strategy for what they want to achieve and how to do it. The companies that get this right have will gain significant competitive edge on their competitors. So the question is; how is your company going to embed predictive analytics as a fundamental part of your customer strategy?
Image: Delaware Consulting
