In the ever-evolving landscape of digital marketing, staying ahead of the competition requires leveraging the latest tools and strategies. One such powerful tool is predictive analytics. Predictive analytics empowers digital marketers to make data-driven decisions, identify trends, and forecast future consumer behavior. This article delves into the world of predictive analytics in digital marketing and how you can harness it to unlock the full potential of your digital marketing efforts.
Different levels of analysis
In the image above, you can see the various types of analytics.
Much of what you see discusses related to digital marketing analytics deals with descriptive analytics, which describes what happened. Bound up with this analysis, if you dig a little deeper than the raw data provided by tools such as Google Analytics and Facebook Insights, is diagnostics. While powerful, this type of analysis can provide insights on a limited number of marketing issues. Examples of insights from descriptive analytics include:
- Which content received the most clicks, drove the most engagement, and led to the most conversions?
- What types of visitors converted at the highest rate, ie. older versus younger, men versus women?
- Did groups of consumers as defined by demographic or geographic variables represent different AOV (average order value), were they more loyal, or did they remain on site longer?
- Which campaigns performed better and worse?
- Were some sources, such as Ads or Facebook, responsible for more visits and/ or higher conversion rates?
- And many more
As you can see, there’s value in these insights that help you build better campaigns, more engaging content, spend your marketing budget more wisely, and focus your efforts on certain groups of consumers that represent a higher value to the organization. These types of analysis are fairly easy, although many businesses, especially small ones, still don’t have a handle on even these analytics basics. As shown in the image below, 45% of firms either don’t measure the ROI of their digital marketing campaigns or don’t know what their ROI is. The rest, except for about 11%, aren’t satisfied with their ROI using existing descriptive tools. Hence, the rationale for moving to more complex and challenging types of analysis, such as predictive analytics, our topic for today, and prescriptive analytics, a topic for another day. These types of analysis offer greater insights leading to higher ROI and greater competitive advantage for your firm.
Predictive analytics involves the use of historical data, statistical algorithms, and machine learning techniques to predict future outcomes or trends. Using predictive analytics in digital marketing means using data to anticipate customer behaviors, preferences, and responses to marketing campaigns to better plan and optimize these campaigns. The defining feature of predictive analytics over descriptive analytics is that the firm uses data to build complex models (called algorithms) to predict future actions. While nothing new, marketers haven’t used these predictive models to their full advantage in part because traditional marketing doesn’t produce the richness necessary to build models that provide insights useful for planning and optimizing.
Predictive models use existing statistical tools such as regression analysis to build a model that represents past data. This is the training phase and results in some combination of variables (factors) and weights assigned to each one that predicts past results. For instance, you might build a model predicting sales that looks something like this:
This model was built using the slope of a line summarizing the data points for sales reps, sales calls, and sales. Plugging the number of sales calls into the model, we find that if a salesperson makes 20 calls, they should sell somewhere around 42 copiers. We can use this information to evaluate salespeople and to project our sales for budgeting (more on this later).
A more sophisticated way to build a predictive model is using statistical software like SPPS or Python. These tools allow models that are more complex and would require building lines in 3 dimensions (ie. involving many variables) and more types of modeling such as maximum likelihood, as well as defining ways to treat missing data. making them far superior to the linear method.
The benefits of predictive analytics in digital marketing:
Improved customer understanding
Predictive analytics enables marketers to gain a deeper understanding of their target market. By analyzing past interactions, purchase history, and demographic data, you can create detailed customer profiles. These insights help the firm craft personalized marketing messages that resonate with individual preferences.
Examples of this include the recommendation engines used by e-commerce firms like Amazon and streaming services like Netflix, as well as the autocomplete used by search engines. Even the search algorithm used by search engines and guides companies in creating high-performing content is a type of prediction of consumer (user) behavior. The purpose of improved customer understanding to craft personalized messaging benefits users, as well, by reducing the effort needed to find the items they will most enjoy from vast numbers of options.
Enhanced campaign targeting
Predictive analytics can identify the most promising leads and prospects for your campaigns. By focusing your efforts on those most likely to convert, you can maximize your ROI and marketing efficiency.
Losing customers is very costly for an organization. Estimates suggest the cost of replacing an existing customer is five times the cost of keeping that customer. In the same article, they provide evidence that increasing retention by as little as 5% can increase revenue by 25-95%.
Predictive analytics can help identify customers who are the most likely to churn before you lose them and what factors might lead to churn so you can fix them before losing any more customers. By addressing their concerns and needs proactively, you can retain valuable customers and reduce churn rates.
Just as it’s important to predict churn, it’s important to predict which customers represent the highest value to the firm so it can engage in efforts to keep these customers, while taking resources away from customers with lower value to the firm. Below, you can see how firms calculate the CLV (customer lifetime value) to help them get and keep the right customers.
Knowing what content will perform well is crucial in content marketing. Predictive analytics can help determine the type of content, headlines, and topics most likely to engage your audience. Multivariate testing using predictive analytics allows you to test an almost infinite number of combinations of these crucial elements to quickly determine the best combination, which reduces your reliance on A/B testing which takes more time. For instance, the new version of Google Ads doesn’t ask users to create discrete ads but multiple headings, descriptions, and calls to action which it then combines in multiple ways to quickly determine which combination will produce the highest click-through rate.
Other forms of optimization also rely on predictive analytics. For instance, airlines use predictive analytics to build complex models that constantly recalculate a ticket price based on city pairs, current percentage sold, historical sales data, and more. That’s why each seat on a plane potentially generated a different amount of revenue and represented a different passenger cost.
Campaign ROI and revenue projections
Predictive analytics can estimate the potential return on investment (ROI) for different marketing campaigns to aid in the budgeting process. This helps a firm allocate resources to the campaigns with the highest expected ROI, thereby optimizing budget allocation. Predictive analytics allows firms to do complex analyses to predict revenue changes based on company actions, such as offering a discount versus a rebate or a discount versus increased advertising spending. Thus, firms can optimize their marketing campaigns to generate the highest ROI possible.
Budgeting begins with revenue projections. Using figures like the one above, firms rely on projected sales as the starting point in determining the marketing budget, in fact, every budget in the firm. If that projected revenue is wrong, everything else in the budget quickly falls apart. Companies don’t have the right amount of inventory to meet demand, don’t have the right staffing to meet the company’s needs, have excess capital that isn’t working for them or need to borrow at the last minute to meet current needs, resulting in higher interest rates, and many other inefficiencies that result when companies budget incorrectly.
Commonly, businesses simply extend a trend line in determining the revenue for the next planning period but there are major flaws with this simplistic method. For instance, changes in the economy like consumer confidence and inflation, changes in customer tastes, changes in competitive offerings, and more. Building a predictive model including the impact of these variables on sales using historical data, means you have a more accurate projection of the revenue in a future period.
Implementing predictive analytics in digital marketing:
Clearly define your marketing objectives and the specific questions you want predictive analytics to answer. Whether it’s optimizing ad spend, improving conversion rates, or reducing customer churn, having clear goals is essential.
Data collection and integration
The first step is to gather data from various sources, including website analytics, CRM systems, social media platforms, and email marketing tools. Combining this data into a single database for analysis requires you to query the various data sources using a key to associate the data files. For instance, your customer database must align with your email marketing subscriber list and results from prior marketing campaigns to determine how customers respond to your email messages through conversion not just clicks and opens. Python or SQL are tools specifically designed to integrate information from databases containing different variables into a single data source for analysis.
The quality and completeness of your data are crucial for accurate predictions.
Choose the right tools
Select a predictive analytics platform or software that aligns with your business needs and budget. Popular options include specialized predictive analytics tools like RapidMiner, IBM Watson, Microsoft Azure, SAP Predictive Analytics, and DataRobot. These tools have many advantages over other modeling tools as they’re easier to use and contain some pre-built models that might solve your problem.
Work with data scientists or analysts to develop predictive models using tools like SPSS or SAS. These models can range from simple regression analysis to more complex machine learning algorithms. The choice of model depends on your data and objectives.
Note that these tools are relatively complex and the menu-driven features allow those without experience to create plausible models with huge flaws when selecting the wrong options. Hence, you should use these tools after sufficient training.
Continuous monitoring and optimization
Predictive models are not static; they require constant monitoring and refinement. As new data becomes available, update your models to ensure their accuracy and relevance.
Predictive analytics in digital marketing is a powerful tool used in the right way by those with adequate training. By harnessing the potential of predictive analytics in digital marketing to guide your efforts, you can gain a competitive edge, improve customer satisfaction, and optimize your marketing campaigns. Start by collecting and integrating your data, choosing the right tools, defining clear objectives, and developing predictive models. With the right approach, predictive analytics can be a game-changer in the world of digital marketing.
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