Data, and tools to analyze that data have been around for quite a while, now. If you have ever run a digital marketing campaign of any sort, you might have looked at performance reports at the end of the campaign. These reports, essentially, tell you how the campaign performed and the things you can improve in future efforts. Thought-leaders, such as Thomas Davenport, have written a lot about how companies compete on data analytics to gain an edge. Predictive analytics takes data analysis one step further. Put simply, instead of looking at metrics after a campaign, predictive models try to guess the future.
For example, a predictive data analysis model could pick patterns that foretell customers that are about to quit your service. As a business, this allows you to reach out to such potential defectors and reduce your churn rate. Predictive analytics in the web domain can, especially, be useful to companies. For instance, with the help of predictive analytics, you can personalize content, thus improving your engagement rates.
The advancements in machine-learning algorithms and natural language processing have meant that web analytics has steadily moved towards being more predictive, in recent years. And certain brands have made the most out of this shift. For instance, Netflix, the streaming giant, uses big data to figure out the shows and movies people would like to watch. In fact, use of predictive web analytics isn’t confined to movie recommendations, alone, at Netflix. It also spills into production decisions and the kind of movies Netflix should be making.
Then there is the case of dating apps playing cupid. Online dating websites, such as Match.com, use data to throw up profiles of people you might find attractive. By taking into account user preference and online browsing patterns, online dating websites try to predict your perfect match. By helping more people find love (or sex) online, they are gunning for better engagement rates. The same goes for Netflix. By constantly coming up with better movie recommendations (based on predictive analytics), the company is targeting to up its stickiness quotient.
The efficacy of predictive analytics isn’t limited to engagement rates, alone. Editialis, a French Publisher, used data and prediction algorithms to improve click-through rates of its email campaigns. The publisher also used data to improve its content marketing efforts, and hence, boost customer acquisition rates.
In order to use the power of predictive analytics, you first need to define the business problem you are trying to solve. For example, if you run a mortgage company and you want more people to sign up for loans, you could do it by:
In the first case, you will need collaborative filtering predictive modeling. Collaborative filtering takes into account historical data, such as buying behaviors, to predict future outcomes. In the second case, though, the cluster modeling technique might be better suited. Cluster modeling is helpful in segmenting people according to personas and demographics.
However, even before you get to what tools to use for prediction, you first need to figure out what business problem to solve. The answer to that lies in where you are at, currently, and your vision for the future. For example, an online educational content aggregator gunning for growth will want to increase conversion rates, dramatically. Similarly, a medium-sized company that’s looking to consolidate its market position will do well to reduce churn rate.
Typically, for predictive analytics to work, you need to follow these steps:
a. Defining the problem and the preferred outcome: For example, reducing your churn rate
b. Collecting data: Decide on the kind of data you want and how you plan to organize it
c. Analyzing data: Inspect collected data for patterns, such as buying behaviors and demographics. This is where your historical data and descriptive analytics come into the picture. Based on the identified patterns, you come up with a set of hypotheses
d. Testing hypotheses: Using various models of statistics, test your informed assumptions and see if they hold
e. Predictive modeling: This is where you choose the weapon of your choice to predict future user behavior. Three commonly used predictive models are cluster modelling, propensity modelling, and collaborative filtering.
f. Prescribing strategic changes: Based on data inferred from predictive modeling, you then make certain changes to your business strategies
g. Monitor, rinse, repeat: Monitor the results from these strategic changes and see if predictive analytics is adding value to your business. If it is not, you might need to tweak the model, or switch to a completely different toolset, altogether
All of that can sound very daunting, and it is. Fortunately, there are ready-made predictive data solutions out there that you can deploy right out of the bag.
Predictive analytics can be applied to almost any business function. For instance, it can be used to optimize project planning in an organization. Data can be used, for instance, to guess the amount of time a planned update will take. Based on the estimate, resources can be allocated to the project to keep things functioning optimally.
Similarly, predictive analytics can be applied to marketing. Here are a couple of examples demonstrating the same:
a. Lead scoring: Your marketing department can use predictive modeling for lead scoring, thus working closely with the sales team to improve conversion rates. For instance, leads can be scored based on how likely they are to buy from you. As an example, someone who visits your product page and your pricing page before submitting a form is more likely to convert than someone who has submitted the form after barely spending 30 seconds on your website. Lead scoring can be combined with predictions about future buying to inform a more optimized content marketing strategy.
b. Customer lifetime value: You can use historical data to predict how much a customer is going to be worth to your business. Based on this information, you can then tweak your marketing budgets for better ROI. It can also be used to predict when a customer might stop buying from you. RFM analysis is one way to predict quitters. It can inform timely intervention that can result in increased customer lifetime value.
As tech giants, such as Google and IBM, compete to build the world’s first quantum computer, significant advancements are expected in the field of predictive analytics and data. For instance, a lot of data analysis tasks might be automated in the very near future. Automation can free up resources to make more strategic, long-term decisions, which means business leaders might be able to harness the power of predictive analytics a lot more.
Similarly, with advancements in natural language processing, we might see a shift towards conversational analytics. You could have a personal assistant, such as Alexa, that spits out predictions as you ask pertinent, complex questions.
Above all else, as data continues to become more structured, we are likely to see the cost of predictive analytics comes down drastically. As that happens, businesses of all sizes are more likely to adopt predictive data analytics more readily, making markets even more competitive. In simpler words, if you are to gain a competitive edge in the present and in the future, it is imperative that you include predictive analytics in your long-term strategy.
At ironFocus, we harness the power of data to help you improve your marketing and sales ROI. We use predictive algorithms to improve customer retention and increase your customer lifetime value, dramatically.