How to Create a Digital Marketing Predictive Analytics Model by @overthetopseo
Predictive analytics is the method of using historical and recent data to forecast future events, trends, and behaviors.
In the context of marketing, predictive analytics involves the application of statistical analysis, machine learning algorithms, and analytical queries to structured and unstructured data sets in order to create predictive models.
These models make marketing planning easier than ever by assigning numerical values to represent the likelihood of certain events happening.
Agreed, this might sound rather technical for a digital marketer, but it’s not.
In fact, analytics master Jeff Strickland simplifies predictive analytics and calls it an area of data science that’s all about creating knowledge out of data to predict outcomes.
This post covers everything you need to know about creating a predictive analytics model to plan and execute your next digital marketing adventure.
Make no mistake, predictive analytics is not simple.
However, with a step by step approach, any marketer can nail it. Here’s how.
Make it S.M.A.R.T – that’s specific, measurable, achievable, realistic, and time-bound.
For instance, increasing sales of high margin products (40 percent or more) by 25 percent within 100 days – that’s a smart goal.
Ask yourself these questions:What are my data sources?What data formats am I working with?What’s the data clean-up workload going to be?How much quantity of historical data I already have?How can I integrate different data channels to feed them all to my analytics engine?
Start experimenting with basic predictive analytics models to understand the challenges you might face on the ground, and to judge whether the results they yield are reliable.
Once you’re confident, start your full-blown predictive analytics model, and keep on measuring results to identify outliers and variables that need to be toned down or removed from the model.
This makes sure that the results you get are more and more accurate with time and data.
The applications of predictive analytics in digital marketing are mind-blowing.
You can easily lay your hands on sophisticated analytics tools specifically made for digital marketing, and also offering intuitive predictive analytics and modeling functionalities.
However, before you buy them, understand how you can shape your digital marketing using predictive models.
Predictive models can capture data relevant to dozens of variables related to user behaviors. This gives companies a deep look into the way users interact, indulge, and engage with their web assets on desktops and mobile devices.
The massive data can then be crunched to illustrate and bring out patterns such as:Timings when maximum gainful activity takes place.The kind of buttons that get most clicks.The kind of banners and sidebar marquee promotions that get most clicks.Usage rates for website pages and features.
Tools like Mixpanel, for instance, let marketers conduct multivariate analyses to figure out retention rates for customers brought in via specific marketing campaigns, and lifetime values.
What if a predictive analytics model could track and capture data related to all the content touch points that a customer interacts with before making a purchase, assign weights to each, and assign proportions of customer’s purchase numbers to each to calculate ROIs?
With tools like Content Scoring, that’s possible. It uses CRM data and marketing automation to track customer journeys, and then assigns values to content touch points such as whitepapers, social media posts, blogs, emails, and e-books) to help you understand which marketing deliverables do and don’t work.
Imagine the kind of sophisticated, targeted, and optimized content funnels you could create to connect prospects to products using such advanced tools.
Though predictive models can result in predictable response rates, they don’t necessarily explain why response rates depend on certain factors.
However, these models can make use of disparate sources of customer data, right from their account information from your database to their website usage sessions, to create highly contextualized and unique personas.
These personas help marketers score substantially higher conversions by targeting the right customers with the right marketing material, promotions, and offers. The characteristics that these models take into account while creating buyer personas are:Firmographic dataPsychographic dataDemographic dataGeographic data
Predictive analytics and modeling are at the core of two important aspects of user engagement during the sales process: upselling and cross-selling.
To come up with high conversion potential upsell and cross-sale suggestions, these models take into account several parameters and data streams such as:Buyer personas and their match with product attributes.Previous purchases, their categories, and general replenishment durations.Browsing and purchase history.Related product purchases are done by buyers from the same segment.
Amazon is at the forefront of leveraging predictive analytics models to create product bundles, upselling and cross-selling suggestions.
Léonard Gaya, heading digital initiatives at Editialis (a French publisher), understands and advocates the use of predictive analytics to engage people in a highly targeted manner.
Gaya admits that the publisher did not use any data-backed models to target email marketing campaigns at customers, and just ‘hoped’ for responses.
However, the team turned things around by implementing predictive intelligence tools. These tools from Sailthru help Editialis to drill down to engagement levels for every individual, and this insight helps them organize content marketing so as to maximize conversion rates.
Right from nurturing leads to converting them into customers, from marketing the right ideas and products to customers to enhancing their engagement with your brands, from maximizing mutual value gains to minimizing bounce and churn – predictive analytics models can help you completely transform every aspect of digital marketing.
More Analytics Resources: