Marketers have long endeavored to use predictive analytics in marketing to forecast customer behaviors, align services and products to expectations, and engage in the most effective marketing campaigns. However, as pointed out by Emerj, these practices were often backward facing. Customers tended to be centrally located and shopped in brick and mortar establishments. Data were collected after the fact and analyzed for their effectiveness at the time. Things have changed. This is how predictive analytics in marketing can be used most effectively to drive performance.
The Current Climate and the Need for Predictive Analytics in Marketing
The structure of marketing changes with any form of customer evolution. Data is now available in massive quantities for far less than what it cost in the past. Sources of such data include:
- Digital channels
- Social media
- Mobile usage
Computing power has also improved to store and analyze such Big Data. Software to collect, analyze, visualize, and report on findings, patterns and trends is now generally easier to use.
As compelling as those reasons alone may be to introduce or enhance the use of predictive analytics in marketing, there remains an even more urgent need for it: the competition. Marketing Evolution reported that with increased purchasing options comes more competition to serve those options and thus, those customers.
Current online trends as compiled by Domo for every single minute of every single day in 2018 include:
- 73,249 online transactions
- 694,444 emails sent per MailChimp (a marketing automation platform)
- 1,111 packages shipped by Amazon
These trends are likely only to increase, meaning that marketers, now more than ever, need the functionality to manage and optimize these opportunities.
What is Predictive Analytics in Marketing?
Predictive analytics combine data, algorithms, and machine learning based on historical and transactional data to create models. These models are then used to infer from those prior events and current transactions to predict future purchasing behavior. And as any savvy marketer will tell you, to predict purchasing behavior, it is critical to have a clear understanding of your customers.
Understanding the Customer
Marketing organizations can build cluster models to develop a full profile of customers, both actual and desired. Customer segmentation, explained Medium, can help marketers better understand their customers by knowing their:
- Media Habits
Knowledge of these attributes can serve the marketer to follow that customer's path to purchase. Also, the marketer can then best target the messaging the customer receives. Solid knowledge about customers enables marketers to root out the causes of customer dissatisfaction (e.g., the possibility of marketing a service the customer has no personal interest in) and churn.
Predictive Analytics in Marketing: Targeted Messaging and Content
To get customers' attention, marketers must break through the ad noise that surrounds them and set your business apart. Towards Data Science reported that personalized messaging could enhance the customer experience, which in turn improves customer satisfaction. Using predictive analytics in marketing allows for assessing the customer’s upselling and cross-selling readiness.
Marketers can upsell the most customized product or service per customer preferences and cross-sell related products immediately. Just as crucial as the customized content is the channel used to deliver it. An in-depth understanding of your customer base means you'll know if the campaign should be by email, social media, or via the company's website. Content should be customized for specific leads.
Qualifying/Prioritizing Marketing Leads with Predictive Analytics
Another type of marketing predictive model for better marketing performance is lead scoring. An unqualified lead can be an unnecessary use of resources (in terms of both time and money). The marketer needs to be able to identify the most profitable customers to nurture them better and increase their likelihood to purchase. Prioritizing these customers optimizes marketing resources and informs the direction of marketing campaigns.
Customer Acquisition/Lifetime Value Prediction
Marketing models can also identify potential new customers. These are typically based on prospects who have attributes similar to existing customers. Once acquired, models can assist marketers in determining their lifetime value.
The lifetime value is the expected length of the customer relationship. Marketers would then want to focus time and money on those customers who are most apt to be long-term (given the high cost of customer acquisition).
Website Optimization and Predictive Analytics
Forbes reports that another essential aspect of predictive analytics in marketing is a solid understanding of how customers use a business website to optimize it. Models can be built to explore how customers
- Engage with the website
- Interact with the website
- Return to the website
- Click through to sale
Businesses can then use the information above to ensure that their website is easy to use, customized to the type of consumer they attract, and leads the casual visitor to convert to a purchasing customer.
Better Marketing Performance – Predictive Analytics Use and Implementation
The use of predictive analytics in marketing to understand the customer, target the identified solid leads and deliver the needed/desired service or product at the right time to the right customer will lead to increased revenues and ROI.
Marketing resources can also be optimized to ensure that the campaigns are timed for the appropriate seasons, environments and demographics. Marketers can also use the models to identify not only current patterns of behavior and predict purchasing behaviors but also potential new trends. This insight then allows the marketer to tweak their product or service to that trend. Then they can get it on the market ahead of the competition.
Implementing the predictive analytic process into the organization consists of:
- Precisely defining the desired outcomes
- Collecting the related data
- Analyzing the data for associations and testing conclusions
- Visualization of results for better interpretability
- Model deployment and maintenance
Predictive Analytics and Marketing – the Right Education
Even with advancements in computing and software, a marketer interested in using predictive analytics in marketing to improve performance by way of predictive analytics needs the right education and training. A data analytics degree is not only desired but needed. In particular, a master’s in data analytics is ideal in that the student can continue his or her marketing career while obtaining the skills required.
Emerson College offers a Digital Marketing and Data Analytics online master’s degree, which covers the science behind predictive analytics skills along with the art of marketing.
Emerson College's faculty possesses the marketing sophistication required to best interpret the information created by the models and how to put that information to best use. The time is now to start seeing the marketing and personal profit from the use of predictive analytics in marketing.