Congratulations! You’ve done the hard part: collecting the demographic, psychographic, and behavioral data from your prospects and customers. Now comes the fun part\u2014spinning it into marketing gold through the magic of predictive analytics. Predictive analytics sounds fancy (and it is), but at its core, it\u2019s about using past data to predict and model future behavior. <\/p>\n\n\n\n\n\n\n\n
If you\u2019re staring at your data like it\u2019s an unsolvable Escape Room, don\u2019t worry. We\u2019re here to help you focus on the right pieces and bring them together to win the marketing game. <\/p>\n\n\n\n
Data is important, but it\u2019s not the only thing you\u2019ll need. Effective predictive analytics requires more than just the data to deliver conversions. Here\u2019s what savvy marketers should bring to the table:\u00a0<\/p>\n\n\n\n
Knowing your audience is step one. Buyer personas provide context for your data, helping you interpret what those numbers actually mean. What is their age, gender, and race? Where do they live, and who do they live with? What are their favorite hobbies and activities? This fictitious description can help you better picture exactly who you\u2019re talking with. And once you outline \u201cThrifty Trevor,\u201d or \u201cLuxe Laura\u201d you can use data modeling to dive way deeper into the data – and Trevor – than you ever imagined. There are over 700 traits and demographics to analyze per household, and this level of granularity changes everything.\u00a0<\/p>\n\n\n\n
What are you hoping to achieve? Lay out your marketing goals and KPIs with specific metrics. Whether it\u2019s increasing leads, bolstering conversion rates, boosting sales, or improving customer retention, your objectives will guide how you will analyze your data. If you don\u2019t know what success looks like, how will you know when you\u2019ve achieved it?\u00a0\u00a0<\/p>\n\n\n\n
The past is a treasure trove of insights. How did your campaigns perform? What worked and what flopped? Historical data helps you identify patterns and trends in your messaging, media, and response rates. You can even match this data up with other first-party data to provide one-to-one attribution of sales and other objectives.\u00a0<\/p>\n\n\n\n
Related: The 6 Marketing Metrics Your Boss Actually Cares About<\/a>\u00a0<\/strong><\/p>\n\n\n\n Predictive analytics isn\u2019t something you can manage with a spreadsheet. Invest in tools that can crunch the large and unruly numbers for you, like machine learning algorithms or specialized analytics platforms. Trust us, your brain (and your team) will thank you.<\/p>\n\n\n\n At Ironmark, we take a multi-step approach to generating customized predictive analytics models. First, we assess what type of data you have. Next, we determine if it\u2019s a good fit for a predictive model. If it is, we identify a variety of models, including: <\/p>\n\n\n\n This data helps us target very specific goals like recruiting, facing a new competitor, increasing sales for a certain product, and more. We combine first-party and third-party<\/a> data to build data models that enable our team to score and rank individuals, customers and prospects alike, to build target audiences for strategic marketing applications.\u00a0<\/p>\n\n\n\n Now that we\u2019ve looked at how to pull the data together, let\u2019s explore the next step, the data modeling phase – where the metrics magic happens as you organize your data to extract insights and make predictions.<\/p>\n\n\n\n It\u2019s amazing what kind of detail emerges with segmentation. Imagine dividing your audience into distinct groups based on their behavior, demographics, or preferences. With predictive analytics, you can forecast which segment is most likely to buy your new product or engage with your latest campaign. Pretty amazing stuff.\u00a0<\/p>\n\n\n\n On the flip side, predictive models can save you some defectors by identifying customers who are likely to leave you for a competitor. Armed with this knowledge, you can intervene with retention strategies, like personalized offers or improved customer service.\u00a0<\/p>\n\n\n\n If you\u2019re in retail, predictive analytics is a gamechanger, helping you anticipate demand to ensure you\u2019re never overstocked or understocked. Say goodbye to warehouse headaches!<\/p>\n\n\n\n Get good at predicting future campaign success with historical data. Will that new email strategy drive clicks? Will your social media blitz boost engagement? The answers are in the data, and this type of confidence can help fuel more successful campaigns down the road.<\/p>\n\n\n\n Now that we\u2019ve seen how predictive analytics works, let\u2019s take a look at an Ironmark case study with real results! Domino\u2019s\u00ae Pizza<\/a> is a global brand, a franchise with more than 14,000 locations across 85 countries. Through strategic partnerships, a data-driven marketing solution was developed for over 1,600 locations across the U.S. Their objectives were: <\/p>\n\n\n\n Deliver Media, an Ironmark company, worked with the Domino\u2019s team to build a series of data models. The approach included merging “customer behavior,\u201d a metric integrating purchasing history with the customer data provided by individual locations, with \u201cconsumer data\u201d comprised of Deliver Media-provided third-party data. <\/p>\n\n\n\n The customer data was robust, capturing prospective customers along with the active customers the stores could be losing or had already lost, or those who had placed big orders. With this model, Domino\u2019s made some key discoveries; they uncovered their ideal customer, identified that customers likely to increase spend also re-engage, executed and tracked campaign effectiveness, and continuously fed the model with response data to optimize it.\u00a0<\/p>\n\n\n\n Related: Domino\u2019s Serves Up Sales With Predictive Analytics<\/a>\u00a0<\/strong><\/p>\n\n\n\n The results were decisive – this strategy produced a direct mail campaign that generated a 16.6 percent higher response rate than previous campaigns, equating to an additional $71MM+ in revenue and an incremental ROI of 606 percent – all due to harnessing their data.\u00a0<\/p>\n\n\n\n Time and again, Ironmark has helped companies reach and exceed their business goals by leveraging their data to power predictive analytics. See how South Moon Under<\/a> realized a 31 percent return on ad spend (ROAS) and Home Instead\u00ae<\/a> gained an average ROI of 1,245 percent. Both companies used data-driven marketing to make real business gains.<\/p>\n\n\n\n But before you jump into the data modeling phase, be aware: not all data is created equal. High-quality, relevant data is the backbone of successful predictive analytics. Here\u2019s why:\u00a0<\/p>\n\n\n\n Having a mountain of data is useless if it\u2019s riddled with inaccurate, outdated, or irrelevant information. Focus on collecting clean, reliable data from first- and third-party sources.<\/p>\n\n\n\n Your data should align with your marketing goals. If you\u2019re targeting millennials, don\u2019t waste time analyzing data on retirees.<\/p>\n\n\n\n The best insights come from combining first-party data (from your own sources) with third-party data (from external providers). First-party data is rich in detail, while third-party data expands your reach. Together, they\u2019ll help you target like a pro.\u00a0<\/p>\n\n\n\nThe Right Tools<\/strong><\/h3>\n\n\n\n
Our Special Sauce\u00a0<\/strong><\/h3>\n\n\n\n
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Data Modeling Examples<\/strong><\/h2>\n\n\n\n
Customer Segmentation\u00a0<\/strong><\/h3>\n\n\n\n
Churn Prediction\u00a0<\/strong><\/h3>\n\n\n\n
Inventory Optimization\u00a0<\/strong><\/h3>\n\n\n\n
Campaign Performance Prediction\u00a0<\/strong><\/h3>\n\n\n\n
Case Study: Domino\u2019s Pizza<\/strong><\/h2>\n\n\n\n
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Why the Right Data Matters<\/strong><\/h2>\n\n\n\n
Quality Over Quantity\u00a0<\/strong><\/h3>\n\n\n\n
Relevance Is Key\u00a0<\/strong><\/h3>\n\n\n\n
STRIKING A BALANCE<\/strong><\/h3>\n\n\n\n