The only constant in the world of marketing is evolution and change. That is why more than ever for businesses to stay relevant and effective, marketers need to stay on their toes. Marketing has already embraced new ideas like digitization, social media, content marketing and automation marketing. The latest piece of the marketing puzzle is predictive analytics. Predictive analytics in layman’s terms use past data and statistics to model and predict the future. It helps marketers account for the variables that can be anticipated, to come up with relevant data to make smart decisions, for specific results.
What is Predictive Analysis?
Predictive analytics is business intelligence technology that uses a predictive model which compiles and interprets volumes of data, learning from the experience of an organization to predict actionable goals. This is not some sc-fi fantasy but already in use in diverse fields. An example of predictive analytics would be the credit score. Complex algorithms process a customer’s data, like credit history, loan applications, employment details and bank transactions to come up with their FICO score, with a prediction about the likelihood of making future credit payments on time.
Most of all of the marketing data being used is backward-looking, whether that is clicks, web visits, web content, embedded videos, downloads, and tweets. Email marketing campaigns produce data about open rates, click-throughs, unsubcribes, and more. Visitor activity on company websites can be tracked, leads flagged for scoring, and also attributed to a particular individual. These are discrete data that are being generated but to provide meaningful insight into the tracked individual, they must be subjected to deeper predictive analysis.
According to a recent Accenture survey of 600 business executives, the use of forward-looking data analysis has tripled since 2009. Predictive analysis is a vast improvement on marketing automation, in adding a huge amount of data and then sifting through all of it to find the most useful buying signals. Marketing organizations are building analytical engines to identify patterns in historical and transactional data, just like the recommendation engines of Netflix or Amazon, where bots crawl the data, find patterns, and predict what you will want to buy next.
Predictive analytics is currently gaining importance for three main reasons:
- Big data has arrived: Earlier marketing data wasn’t easily available to confidently predict the future. Today, companies and individuals have sheer volumes of data and information available through social networks, on the Web, and in internal systems (such as CRM and purchase histories).
- Advancement of Technology: Advances in technology mean companies can cost-effectively harness, capture, store, search, share, analyze, and visualize data. It is within the reach of most organizations to use new technologies (Hadoop and text analytics) to process both structured and unstructured big data.
- Skilled Personnel and Software: Predictive analytics is no longer the domain of skilled data scientists. Now graduate students too are working on software’s to run conditional mutual information algorithms, random forests, and neural networks, to make it more accessible to more organizations.
Why Use Predictive Analytics in Marketing?
- Better Results through Predictive Analytics Predictive analytics help companies score higher in two important marketing metrics, incremental sales and better click-through rate. Targeted specific marketing campaign by accurate segmentation of the larger market, with the help of predictive analytics sees an incremental sales rise and an average click-through rate from mass marketing campaigns that is 76% higher than that of non users.
- Higher Marketing ROI An effective marketing campaign needs to address multiple social channels and incorporate data from them to effectively target the buyers therein. Predictive analytics enable precise segmentation of potential buyers and are able to create unique customer profiles, with improved targeting of marketing offers. It provides an insight into these buyers, their needs and their motivations, allowing for better optimization of the marketing offer and message directed at them. Predictive analytics helps gain improved marketing ROI-as evidenced by the superior click-through rates and incremental sales lift.
- Identify Best Leads to Improve Customer Acquisition The marketer’s goal is to maximize the lifetime value of customers at minimum spend by efficiently acquiring high-value customers. Predictive market segmentation identifies the size of potential markets, so territories can be distributed fairly and investments can be made keeping in mind the revenue opportunity available in each segment. Predictive analytics comes in handy for improved customer acquisition, where for example you have two customers. The first costs $30 to acquire as a customer, versus the second who costs $50 to acquire. Big data and predictive analytics can now determine the predicted lifetime value of the higher cost-per-acquisition customer as against the lower cost one to generate better leads.
- Retain and Reactivate Existing Customers Predictive analytics optimizes marketing campaigns and website behavior to keep existing customers, and retarget those customers who are no longer actively buying from your company to increase customer responses, conversions and clicks, and to decrease churn. Each customer’s predictive score informs specific actions to be taken, whether it is through product recommendations, clustering or understanding propensity by behavioral attributes. In the case of reactivating old customers predictive analytics allows a surgical pricing, or discount offers, while optimizing the inventory.
- Predictive Social Analytics Strategic marketing sees social media not just as a place for conversations around their products and services but also for listening and engagement. Complete marketing ecosystems not just monitor social media engagement, but also generate sales leads, respond to customer service queries. Big data and predictive analysis of social media helps uncover the products a customer is interested in, the keywords they are typing into search engines, and what they purchased to draw up recommendations of what that customer might do in the future. This shapes marketing campaigns, as well as future product strategy. Technologies like text analysis and sentiment analysis can extract additional meaning from the social media, valuable data that many organizations are just starting to access.
In a few years predictive analytics is likely to become a staple marketing technology but for now early adopters can reap huge sales increases, to leave their competitors behind. If you are looking to be in front, give us a call. We can help.