Predictive Analytics for Marketing

In the era of a pandemic, medical care can become a ubiquitous metaphor for many aspects of life. So is it with analytics. Think of predictive analytics as a preventive vaccine that can save you from disease. On the other side of the fence is retrospective analytics, which can serve the purpose of a postmortem and at best offer hindsight. In between the two, of course, lies real-time analytics, the diagnostic version of an emergency vehicle, akin to getting access to medical aid before time ran out.

The growth of Predictive Analytics is inextricably linked with the rise in Big Data. With a reported over 2.5 quintillion bytes of data created every single day, it stands to reason that data analytics only continues to evolve to help in predictive decision making. By integrating techniques such as data mining, statistics, modeling, machine learning, artificial intelligence and more, it helps mitigate future risks and identify prospective opportunities. 

Predictive Analytics: Data Mining to Predict Trends & Behaviour

The fact, however, remains that predictive intelligence as a concept isn’t something new or path-breaking. For years we have used statistical forecasting to try and navigate the fog of the unknown. However, it is only when businesses have been able to leverage high processing speeds, complex algorithms, data mining infrastructures, predictive software and more, that predictive analytics has come into its own. 

Across enterprises, predictive intelligence is being used for a wide set of applications. Take the case of banks and financial institutions, predictive intelligence is being deployed to detect frauds and address compliance issues. On the retail side, AI & ML is being used to forecast demand right down to specific SKUs. Manufacturers are using it to forecast the failure of equipment and thus mitigate downtime. Even professional sports teams are using analytics to forecast injuries of players. 

Obviously, it will be worthwhile to explore how predictive analytics can add insight and clarity to your marketing decisions; because the future is now! In this article, we use our expertise and experience to highlight some of the most noteworthy marketing applications and approaches to predictive analytics. 

Current Applications of Predictive Analytics in Marketing 

Breaking down basics; predictive analytics uses data, AI, ML & statistical algorithms to plot a likely outcome, based on historical data. It goes beyond an assessment of “what happened” and focuses on the “what will happen”. Here are 5 such applications that marketers must be aware of today. 

  1. Predicting Consumer Behavior Accurately

Consumers being the raison d’etre of enterprises, customer intelligence has to be the most important aspect of marketing decisions. Predictive analytics helps build robust customer personas and also uncover traits of consumers with high revenue potential. A huge part of the power of using these analytics is also that you can gain an insight into the unique context in which customers make buying decisions. Importantly, you can also use predictive analytics to identify dissatisfied consumers that you could stand to lose. Essentially predictive modelling for customer behaviour widely rests on the following models: 

1. Segmentation modelling that groups customers based on various factors such as their demographics or their average order size.
2. Predictive modelling that can be used to predict aspects such as:

• Conversion propensity
• The predictive lifetime value of the consumer
• Churn or attrition rate. 
• Recommendations modelling, that largely involves the ability to recommend the right products and services to consumers basis, among other things, their past buying behaviour. The payoff of personalization and recommendations done well is the ability to up-sell as well as cross-sell products. 

 

  2. Identifying and Prioritizing Qualified Leads

Ask any salesperson and he can vouch for the time and money wasted on unqualified leads. With predictive analytics, the process of qualifying leads can become more than relying on just an anecdotal list. Predictive analytics can help qualify leads based on a wide range of demographic, behavioural and psychological data. In turn, enabling the sales teams to nurture potential customers. Three categories of marketing use cases that are known to deliver results, as per a Forrester study include:

• Predictive Scoring, which involves prioritizing leads based on their probability to take action.
• Identification Models, which involve identifying prospects with the desired behaviour. This can, among other things, help marketers to prioritize existing accounts or even uncover new markets.
• Automated Segmentation, which essentially involves using uncovered attributes to drive outbound communication with relevant messages that resonate with the target consumer.

3. Launching the Right Products

A Deloitte study, has found that nearly 96% of product innovations fail to return the cost of capital while two-thirds fail within two years. What is required, therefore, is a data-driven understanding of customer expectations, to model products that suit their needs. In fact, the power of predictive analytics grows exponentially when an organization takes an end-to-end view of new product development including testing and launch. 

A case in point would be Netflix’s use of new product development analytics. The company is known to classify the attributes of past and current products and then model the relationship between the attributes as well as the commercial success of the product. The FMCG major, Procter & Gamble is also known to use predictive analytics while using data from multiple sources such as focus groups, social media and more to introduce new products that have a strong likelihood of success. What this effectively means is a significant enhancement in innovation and hence improved competitive advantage.

4. Driving Marketing Strategy

All of these activities go a long way in devising an effective marketing strategy including the most important channels to use to reach the target consumer, the type of content to be served and even when the audience should be targeted. Using behavioral data along with customer journeys, you can predict engagement points for client conversion as well as drop-off points. Additionally, with exact reporting, you can accurately figure out the success or failure of a campaign and optimize where it falls short. 

5. Customer Targeting and Messaging

With customer acquisition costs becoming a major expense head, the one major advantage of predictive analytics is that it can help identify and target customers who are most likely to buy or subscribe to the services. By specifically targeting these customers with the right messaging, marketers can significantly increase the return on their marketing investment. 

In terms of messaging too, predictive analytics offers the opportunity of implementing personalized campaigns.  In fact, these could even go to the extent of offering product suggestions at the time when the customer is likely to run out of the product or offering a discount coupon when he is likely to be walking by the store.

What the marketer is looking at, therefore, is:

• Improved conversion ratios
• Improved profitability & operational efficiencies

 

Predictive Analytics – The Marketing Tools you need

Data

Businesses need to have adequate training in data sourcing and mining, which can potentially be used to build predictive models. All data need not be internally mined, a lot of external sources can also make critical additions to the dataset. Stitching data sources together is a major task that needs to be undertaken with a lot of precision, since predictive models are only as accurate as the data they’re built upon.

Data Scientists/Software Suite

To turn data into predictions, the next critical requirement is that of a robust software suite or experienced data scientists to mine this data correctly.  For most businesses, this would translate into either of the two options: 

•     Purchase the software and use it to create predictions in-house
•     Partner with vendor agencies who have a track record of successfully developing, deploying & scaling these models.

Success Stories of Predictive Analytics

The proof, as they say, lies in the pudding. Here are some examples of enterprises, which have effectively used predictive analytics to up their game.

 

Amazon

No conversation on the success of predictive analytics is complete without the mention of Amazon and not just because they have been early adopters. Not only do they have a robust recommendation engine that offers personalized recommendations basis wish lists, items reviewed, what others have purchased, they even have a patent for a forecasting model. With the use of predictive analytics, the goods that you are likely to purchase are sent to a local distribution centre, so that they are ready to be dispatched to you as soon as you place the order. Overall, therefore, Amazon uses predictive analytics effectively to increase its product sales and profit margins while decreasing its delivery time and overall expenses

 

Facebook

If there is one word to describe Facebook’s use of analytics, it has to be relentless. It’s not surprising given the fact that here is a company whose business model revolves around effective mining of data. Sample this- In a blog post titled “The Formation Of Love” a Facebook data scientist talked about how a user’s posting patterns and moods can be used by Facebook to predict their future romantic relationships. The post reads- “During the 100 days before the relationship starts, we observe a slow but steady increase in the number of timeline posts shared between the future couple. When the relationship starts (“day 0”), posts begin to decrease. We observe a peak of 1.67 posts per day 12 days before the relationship begins, and a lowest point of 1.53 posts per day 85 days into the relationship.” So much for Facebook playing Cupid

One of the other notable use cases, of course, includes Facebook using deep neural networks to decide which adverts to show to people that could resonate with them and lead to action.

 

Nissan Motors

The use of predictive analytics is by no means restricted to new-age tech companies alone. Nissan Motor Company, is known to use Predictive Analytics to tailor advertising campaigns to suit the needs of a region by delving into car types, models and colours that people of a region have been looking at, online.

 

Unyscape’s Success Story with Predictive Analytics in Marketing

 

Pfizer

Speaking of high feats in predictive analytics, Unyscape’s own success story with Pfizer is worth a mention. The world’s largest pharmaceutical company came to us with a problem – Pfizer’s Forecasting solutions, developed in 2016, was based on legacy forecasting techniques and relied heavily on POC interaction and business questions. Due to the unpredictable nature of the business, the model  ended up either creating a backlog or inaccuracy if time was of the essence. This is yet another example of implementation challenges of predictive analytics. 

Unyscape gathered their experienced team to expedite the process of identifying key trends from raw data to further help understand each SKU’s position in the market. Improved accuracy of the forecasting methods using the domain’s understanding.  An established model was created to identify and address issues at the stages of pre-processing, processing and post-processing individually. The results – 100s of enhancements delivered above the legacy forecasting models and 1000s of hours saved by the implementation of Unyscape’s Model at data insights and trends identification.

Challenges in Implementing Predictive Analytics 

While the use cases of predictive analytics are undisputed, predictive analytics comes with its own set of challenges. Key among them being:

1. Incompleteness of Data

The accuracy of predictive analytics models is limited by the accuracy of the data being used. The incompleteness of data, data silos can render the analytics deficient and the predictive patterns, inaccurate. Similarly, data myopia may lead to the limited classification of data that can impact the precision of the model

2. Customer Spookiness

Automated systems are increasingly capable of scanning customer interests. However, it’s also a fact that customers are increasingly being unnerved by the anticipation of their intent. This could influence their customer journey, leading to false or inaccurate data collection. 

3. Expertise 

As a subject, predictive analytics is complex and requires a deep understanding of statistical modelling. Without the required expertise, most organisations are there limited without the right expertise & knowledge. 

4. Scalability

Most of the time, when a predictive model is developed, it is close-tested time and again to prove its success. However, the success gained during testing is not necessarily mirrored when the model is scaled and applied on real projects. Most organisations struggle in creating scalable systems that are capable of providing a multi-dimensional application of big data systems & applications.

5. Action

The role of predictive analytics is to deliver information and give the right insights. But, without the right follow up action, these insights are useless. Organisations fall short without the right follow-up actions. For the true success of predictive analytics, you need intelligent workflows put in place. This means, either setting the protocols for the next step or setting up the trigger for the next process. 

Organizations must ensure that the right data is collected, that the models are complete and accurate and that the models are used at the right time and place. 

 

The Future of Predictive Analytics

Once upon a time, oil was considered to be the world’s most valuable resource. Today, of course, data has emerged as the new oil. The key to unleashing the potential of data, however, much like oil, lies in refining it into actionable business insights. The fact, however, remains that predictive analysis is still in the early stages of its adoption cycle. With the cost and technology barriers to its adoption, however, significantly reducing, its adoption will only increase in times to come. In fact, accurate and timely business intelligence will be the biggest differentiator of successful businesses. Going forward, in fact, predictive analytics will further evolve into prescriptive analysis, whereby marketers not just determine what will happen but also how they can make a certain outcome happen