Predictive analytics is probably the hottest thing in marketing analytics right now. Predictive analytics go beyond describing consumer behavior to predicting how consumers will behave in the future based on data.
If you have never heard of it, don’t feel bad – marketing lags behind many other business disciplines in using data to drive decision-making. In fact, marketing still struggles with descriptive statistics, which merely describe behaviors, such as visits, ad responses, etc.
Marketers shouldn’t feel bad about their reliance on squishy analytics, such as increased awareness. In the past, marketers just didn’t have access to data, which was locked in the minds and hearts of consumers. Gaining access to this data was very expensive and time-consuming, forcing marketing managers to use less accurate and nuanced data such as customer demographics and point of sale data. But, with digital marketing, marketing managers now have access to a plethora of valuable data and many. But, with digital marketing, marketing managers now have access to a plethora of valuable data and many struggles with how to use it although that access might be restricted to data analysts and they may lack the skills necessary to draw insights from the data.
What are predictive analytics?
Put simply, predictive analytics, as their name implies, try to predict the future, while descriptive analytics describes the past and prescriptive analytics help plan the best course of action
Benefits of predictive analytics And, based on survey data, Forbes says:
“A vast majority of executives who have been overseeing predictive marketing efforts for at least two years (86%) report increased return on investment (ROI) as a result of their predictive marketing”
Only 5% say they’ve not experienced an improvement in ROI or a decline in ROI from their efforts at predictive analytics.
Uses of predictive analytics in marketing.
Arguably, predictive analytics support most enterprise activities, but I’m focused on marketing. Here are some uses for predictive analytics in marketing:
1. Segmentation
Segmentation involves separating a market into subgroups with similar attitudes, demographics, geographics, or behaviors. After segmenting your market, you position your product to appeal to the wants and needs of the chosen segment(s) – your target market.
Data aids in crafting your target segment(s) and determining the most effective positioning for each. Predictive analytics also helps to identify the most profitable segments based on historical consumer behavior within each segment.
Marketing managers use this data to allocate resources to reach the most profitable segments.
2. Forecasting
According to HBR, the biggest use of predictive analytics is in developing demand models that forecast sales and revenue – the starting point for budgeting.
3. Demand pricing
Demand pricing, often called yield management, involves pricing products based on differences in elasticity of demand between consumer groups. For instance, business travelers are willing to pay more for a seat on an airplane than casual travelers, so you can charge them more and reduce the price to casual travelers to make your flights more competitive and still meet ROI goals.
Using predictive analytics, firms conduct a series of experiments to determine factors affecting the impact of price on demand. Using these predictive models, firms develop optimal pricing strategies that maximize ROI.
4. Improve customer satisfaction
Customer satisfaction greatly impacts retention and loyalty. It also improves other positive consumer behaviors, such as recommending the brand to others. Any improvement in customer satisfaction impacts ROI, potentially significant. That’s the entire basis for the rise of relationship marketing, where marketing shifts its focus to pleasing existing customers than attracting new ones. Data suggest that it’s 5X less expensive to keep an existing customer than replace that customer.
Using data from customer service calls, mentions on social media, etc offer insights into factors leading to poor customer satisfaction. Using predictive tools such as conjoint analysis allows firms to discover which product improvements generate the greatest improvement in customer satisfaction.
Why predictive analytics are useful?
Predictive analytics not only describe what’s happening, but they also predict what WILL happen in the future, which is REALLY valuable stuff. Here are just a few things you might want to predict:
Implementing a predictive analytics solution
Step 1 Identify your business problem
Step 2 Determine what metrics are necessary to address your problem
Step 3 Determine which analysis technique you’ll use (determines the amount of data necessary)
Step 4 Collect historical data on all necessary metrics
Step 5 Analyze the data including assessments of data quality
Step 6 Communicate findings to organizational decision-makers
Step 7 Implement decisions based on findings
How businesses use predictive analytics
Businesses use predictive analytics in a number of ways, such as the one discussed above. In addition, a number of tools, such as CRM (Customer Relationship Management) use predictive analytics to determine marketing strategies. Another type of predictive analytic is CLV (Customer Lifetime Value) which uses purchase information to classify customers into groups and determine the level of profit reflected by each group, which is used to build marketing strategies for each group.
Predictive analytics and social media
Marketing in general, and social media marketing in particular, are not heavily influenced by predictive analytics. Although, that’s changing as supercomputers allow organizations to use massive data captured during transactions to build predictive models of what consumers buy and factors that impact their purchases.
Still, relatively few companies use predictive analytics to drive marketing strategy. Sometimes, when I pitch to prospective clients, I’m shocked at how few firms demand any true analytics from their agencies and almost none even understand the concept of predictive analytics. If the agency provides any analytics, it’s commonly simple ones such as # of Fans, # or RT, or other somewhat meaningless data.
Agencies and in-house marketing employees often develop simple correlations as a way to build a social media marketing strategy. For instance, they might notice that certain types of content drive more engagement or that posting at certain times generates more engagement, so they do more of this. But, this lacks the depth of understanding necessary to build predictive analytics.
Source: https://www.hausmanmarketingletter.com/predictive-analytics-improve-roi
