7 Steps in Predictive Analytics: Moving Beyond the Conversion Funnel photoDigital marketing reached an inflection point about a year ago. It grew up and realized it had to cast off its childish ways of just “playing around” on social networks and creating pretty pictures on websites and apps. Maturity came with the realization that ROI matters and generating lots of visitors or Fans didn’t translate into coin in the business’s coffers. Animated GIFs and fancy website features didn’t generate money to pay bills.
Over the last couple of years, firms realized they needed metrics to monitor their digital marketing efforts and make sound decisions that optimized their ROI.
Firms using analytics to make decisions earn 220% higher ROI than firms that don’t
Today, if you’re not assessing your campaigns, you’re likely wasting your time and money!
A host of tools emerged to help firms and Google Analytics introduced new features — today, they introduced content grouping — into their free analytics tool.
Tools focused on descriptive analytics — who, what, where, how many. Armed with this data, firms gleefully crafted conversion funnels — like the one above — showing how efficiently they moved consumers down the funnel until some ultimately bought the product or service.
Conversion funnels are fabulous tools showing firms where buyers come from, where visitors drop off, and underscoring problems in the buying process.
But, IS THAT ENOUGH?
Moving beyond Sesame Street
Just like Sesame Street provides the building blocks you need to read, compute, and get along with others, these descriptive analytics are valuable. But, knowing the letters of the alphabet and counting aren’t enough to succeed in today’s complex world, and descriptive statistics aren’t enough to succeed at digital marketing.
7 Steps in Predictive Analytics: Moving Beyond the Conversion Funnel photoA recent article in Forbes offers a use case of predictive analytics and its impact on ROI for mindjet. This graphic shows the process of collecting and analyzing data to score leads that optimized sales contacts.
Lead scoring is just 1 example of predictive analytics — if you’re interested, I developed a proprietary algorithm that works great for lead scoring.
What makes predictive analytics different?
Rather than just describing the who, what, where, when of your social media campaigns, predictive analytics PREDICT which actions will generate the best return based on an algorithm, such as a regression equation. As portrayed in the image, predictive analytics require collecting existing (descriptive) data using tools like Marketo, Salesforce, Eloqua, and Oracle. Data is run through a tool like Lattice, SPSS, or SAS to find patterns (correlations/ covariance) in the data. Alternatively, my proprietary algorithm uses an econometric model to build out a sophisticated predictive model. I’ve done the same thing in building my 4-factor model of social media conversion.
Why predictive analytics are useful?
Predictive analytics not only describe what’s happening, they predict what WILL happen in the future, which is REALLY valuable stuff. Here are just a few things you might want to predict:
- Lead scoring — which leads are most likely to convert
- Consumer behaviors — if you do X, consumers are likely to do Y
- CLV (Customer Lifetime Value) — which assesses how AGRESSIVELY you should pursue a group of customers or prospects
- Optimal frequency for posting
- Optimal pricing
- Customer defection – which customers are most likely to defect
How to build predictive models?
I commonly use a variety of tools. Normally, I’ll start with SPSS to determine the data quality — remove “bad” data, check normality, etc. If necessary, I’ll transform the data (for instance mean centering) and explore the relationships using correlation analysis. If I have a hypothetical model, I’ll test it with the data, if not, I’ll build a model with 1/2 of the data, then validate the model with the other 1/2 — called a jackknife approach.
I use regression (to build linear relationships), cluster (great for finding groups or segments of consumers), and sometimes analysis of variance to understand how groups behave different.y
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
Whether you need a complete content marketing strategy, some help with Adwords, or some consulting to optimize your existing social media marketing, we can fill your digital marketing funnel. We can help you do your own social media marketing better or do it for you with our community managers, strategists, and account executives. You can request a FREE introductory meeting or sign up for my email newsletter to learn more about social media marketing.
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