Predictive intelligence isn’t exactly an avant-garde approach. For decades, analytics tools have been utilized by businesses to monitor and ‘predict’ many factors such as frauds, assess risk, and even for timely maintenance.
However, the analysis of this data has been a tedious function simply because the technology wasn’t advanced enough. Imagine having to compile the names of the Presidents of 100 given countries but having no access to the internet to do so. Sure, you could refer books, but the process is much simpler with the advent of newer, more intuitive technologies.
Though that example is considerably primitive, a similar obstruction is what caused predictive analytics to not influence the marketing terrain like it is presently doing.
Thanks to the advancements in artificial intelligence and AI-powered marketing resources, such obstructions are no more. Chatbots, content curators, dynamic pricing models, and a plethora of other AI marketing tools have made predictive analysis accessible, affordable, effective, and much more profitable.
To make a bit more sense out of this, let us try to define predictive analysis and predictive marketing.
Basically, predictive analysis is understanding what has happened, to estimate what will happen. This can be done using data, machine learning techniques, and statistical algorithms which produce more constants than variables.
And, predictive marketing uses these insights and anticipatory specifics to determine the best action plan to be deployed for maximum returns. Simple right?
Easier said than done.
So how exactly can collecting humungous amounts of data (big data) and adopting a predictive approach help businesses and brands?
Seems like a stupid question because this right here is the future and in many ways, the present, of marketing. It is about staying one step ahead of the competition. But how can we stay ahead of a competition which is already up to date?
By going beyond the date. By accurately seeing the future. By accurately measuring subsequent outcomes based on past behaviour.
Let us discuss three basic and broad applications of predictive marketing.
Foreseeing customer behaviour
By identifying patterns in past behaviour, decisions, and browsing habits, predictive modelling can assess a customer’s future behaviour such as what they might be interested in seeing, the best time to reach out to them etc. You can also distinguish between satisfied and unhappy customers and model your marketing strategy accordingly. Customer intelligence is the core from which marketing strategies draw power.
Distinguishing qualified and unqualified leads
Every business detests unqualified leads. Spending time and money on people with no intention of purchase is an absolute waste. By formulating algorithms that can be fed data from predictive analytics in order to separate qualified leads form qualified ones, we can prioritize prospects according to their convertibility. Identifying prospects with maximum potential can also help the sales team to recognize where to focus more.
Businesses with slightly shallow pockets will find this extremely helpful as it enables them to accurately allocate their funds and resources.
Customized personal messages is a huge driving factor. Being able to address the clients or customers on a personal level and tailoring content to fit the individual expectations of each of them is a plus in marketing. Manual methods of personalization such as sending individual emails are outdated and honestly not that viable.
Now, marketers are depending more than ever on AI and predictive technologies to automatically understand and adjust to the needs of various customers and clients and to formulate content in a way that appeals to each and every one of them. After all, customer experience is of paramount importance.
The aforementioned points are very broad. Predictive analytics has sunk much deeper into the roots of marketing and is a major influential factor in every step and every decision.
Let us now take a look at 3 ubiquitous predictive models. This includes:
- Segmentation Model
- Discerning Recommendations
- Susceptibility Model
Customer segmentation has come a long way. From a logical estimate to a deliberate affair based on data, facts, and statistics, customer segmentation is now a quantifiable force upon which many major decisions and strategies are built.
There are 2 broad spectrums into which customer segmentation can be classified. This includes Behavioral Clustering and Product-Based Clustering.
The data of a lead converting into a customer is extremely valuable for any marketer. However, in isolation, this data is pointless. Combining it with demographic and firmographic data can help marketers to narrow down their search for new targets by identifying the common factors. This is called Behavioral Clustering.
Product-based clustering has a much more heterogeneous approach to demographics. This is based on the product-interests. Customers who were or are interested in similar products are grouped together. This helps in building a portfolio of your most saleable products or services.
This model is based on recommendation filtering. This is similar to product-based cluttering but is more focused on an intermediary execution level. This model helps in understanding the potentials of your product for upselling or cross-selling it.
This is effectively exploited by Amazon. By showing a list of recommended products, or combining it with the products that others with similar interests have purchased, the e-store is instigating a choice of additional purchase during the process of purchase itself.
The Susceptibility Model
This model depends on the data that indicates the tendency of various users to behave in a particular manner. It includes Share of wallet.
The share of the wallet is basically an estimation of the budget allocation a customer has done, for your product or service. The higher the number, the lesser the potential of cross-selling or upselling. Essentially, combining this data with the product-based clustering can tell the marketers what products to show the customer to increase your share of their wallet.
To sum up
Predictive analysis and acting upon anticipatory intelligence is a crucial element in marketing. It helps to understand your audience better. To correctly guess what he or she is willing to spend their time and focus upon and to provide them with content that appeals to them in a very personal, and consequently in a very actionable manner.