![\"Predictive](\"https:\/\/www.smartinsights.com\/wp-content\/uploads\/2019\/06\/Predictive-analytics-infographic.png\")
<\/p>\nIn case you haven\u2019t spent much time with web analytics, I\u2019ll start by giving you this definition of predictive analytics (PA), courtesy of Wikipedia:<\/p>\n
\u2018Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns.\u2019<\/p>\n
Whereas with descriptive statistics you learn what data can and can\u2019t describe your customers\u2019 behavior, with predictive analytics you learn what to do next \u2014 that is, use data about past actions to make predictions about future actions.<\/p>\n
Obviously, if you can discover and anticipate what your customers \u2014 and earlier-sales-stage prospects \u2014 really want from your brand, you\u2019ll hold the \u2018golden ticket\u2019 to higher revenues and customer loyalty. As a result, you\u2019ll become a rockstar analyst on your team.<\/p>\n
This graphic below shows how three well-known brands use PA to serve up more customer-centered experiences.<\/p>\n
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In what follows I\u2019ll first share the quantitative benefits of using PA platforms.I\u2019ll then discuss the qualitative (and highly strategic) benefits of integrating these apps.<\/p>\n
Most e-commerce companies focus a lot on the Revenue Per Visitor (RPV) and Monthly Recurring Revenue (MRR) metrics. But boosting recurring monthly profits is even better. PA apps can help you do this by 1. driving up Average Order Value (AOV) and 2. offering optimal pricing for each visitor.<\/p>\n
First, let me define what I mean by \u2018up-sell.\u2019 An up-sell happens when a brand offers one or more complementary products (or accessories) together with the base product. If, after you\u2019ve just decided to buy a new bike, the sales clerk says, \u2018Here are the helmet and gloves I recommend to go with it,\u2019 he\u2019s doing an up-sell (actually two of them).<\/p>\n
Up-sells are the \u2018low hanging revenue fruit\u2019 of e-commerce selling. Why? Because your prospect needs them to get a complete solution (for the previous example, to bike around the neighborhood safely). So, the better you can offer the right accessories at the best time and place, the more likely it is that a given visitor will add one or more of them to her order.<\/p>\n
That\u2019s where PA platforms come in. Based on the interaction and transactional data it collects over time, a PA algorithm can first determine in which segment a given visitor fits. During the user\u2019s first visit, the algorithm can then show some \u2018best fit\u2019 accessories for the base product being shopped.<\/p>\n
Then, on Visit number two, the algorithm - based on what was learned from the user\u2019s mousing and interactions - can serve up an even better set of accessories. If your PA platform supports it, the tool can test different placements of the accessory products. Over time, these continually-better-tuned algorithms nudge your visitor toward clicking those \u2018add item\u2019 or \u2018add bundle\u2019 button. This, in turn, lifts your AOV.<\/p>\n
In case you didn\u2019t know, there\u2019s a lot of price finessing going on in the world. For example, the person sitting next to you on your previous flight probably didn\u2019t pay the same airfare as you did (perhaps even a lot less). Airlines were forerunners in the \u2018dynamic pricing\u2019 realm, and e-commerce PA platforms have now taken this technique to the next level.<\/p>\n
For example, if you\u2019ve visited an e-commerce site three times, signed up for its \u2018special offer\u2019 emails, viewed a particular product page three times, and hovered over the \u2018add to cart\u2019 button twice, you obviously have a higher engagement level than a first-time visitor who hasn\u2019t yet hit a product page.<\/p>\n
In such cases, a smart PA platform could push a message saying, \u2018I see you\u2019ve viewed [product X] a few times. How does a 15% discount sound?\u2019, after which you could offer three choices:<\/p>\n
Some PA platforms will even adjust the pricing for you automatically. Let\u2019s say, for example, that your visitor lives in a high-income Zip Code. You could offer this visitor the product with a default price of $XX. But the algorithm could offer a visitor from a middle-class Zip Code their the product at 0.85 * $XX. (Assumption: In both cases, the company would earn a good profit margin.)<\/p>\n
On the qualitative side, the benefits might not boost your tactical KPIs, but they can certainly inform your future marketing tactics and boost your team\u2019s operational efficiency.<\/p>\n
Since all of the leading PA platforms include reporting capability, you can easily run reports that show things like:<\/p>\n
For example, let\u2019s say you discover that (transacting) customers who view videos have a 20% higher RPV. You should then direct your designers to make the \u2018play video\u2019 interaction more enticing (for example by making the \u2018play\u2019 icon more visible, or the splash image more interesting).<\/p>\n
Knowing these things \u2014 which amount to business intelligence \u2014 you can then hypothesize as to why these correlations are happening, and dream up new A\/B tests ideas that capitalize on these findings.<\/p>\n
As we know, working in a golden age of data-driven marketing, applying data and critical thinking to inform our marketing strategies can turbocharge marketing performance. However, misinformed strategies or decisions made based on misleading analysis can spell disaster later down the line.<\/p>\n
Our RACE Framework is a simple strategic marketing structure designed to help marketers plan, manage, and optimize their marketing strategies using key insights from their customers' journeys.<\/p>\n
Book your free 1-2-1 consultation call with a member of the team today and take the chance to revisit your marketing strategy, and discuss your opportunities within the context of the RACE Framework. Get started today Book your free 1-2-1 consultation to develop your new strategy with the RACE Framework<\/p>Book consultation<\/a><\/div> Without a \u2018core\u2019 PA platform, your marketing optimization team requires at least these people: an optimization analyst, a user experience designer, a software developer and a product owner (to negotiate with the business which tests to run). So you can best make sense of the reams of visitor and customer data flowing in daily, a data scientist would be great to have as well. These people would need to be proficient with a growing set of tools.<\/p>\n It\u2019s a lot to manage. Besides that, the stakes are high. A failed split test or two could set your team back, both in terms of schedule and political clout.<\/p>\n That\u2019s why these PA platforms are so valuable. By integrating your current analytics, predictive analytics, marketing campaigns, split tests and reporting into a single suite, they reduce the size of your team and keep everyone on the same page. Sure, PA apps do require a couple of weeks of learning ramp-up time, but once you\u2019ve done that your team can focus on your core tasks of designing campaigns, running optimizations, and collecting insights.<\/p>\n I get it; what your CFO cares most about is financial results \u2014 in the form of revenue growth, improved operational efficiencies, and return on investment (ROI). So I\u2019ll share some results, achieved through the use of two leading PA platforms.<\/p>\n Here\u2019s an example case study from HiConversion, a PA platform that\u2019s been around for over 10 years.<\/p>\n For the parent company of UGG Australia, a seller of fashionable boots and footwear, HiConversion\u2019s platform ran several variations of the \u2018Heirloom Lace-up Boot\u2019 product page. Their algorithm discovered a new version of the page that produced a 14.73% revenue lift within a six-week timeframe. In addition, the study found that the single biggest contributor to conversion lift<\/a> came from a very small change in the look and feel of the checkout button. (In the chart below, \u2018C-buttons\u2019 refers to the checkout button colour.)<\/p>\n Something that\u2019s important to reiterate: this revenue gain was for a single product page. UGG could run similar tests on other product pages, or pages leading up to them, over time, to produce additional revenue lifts. They could apply these learnings to other buttons on their visitors\u2019 paths to this conversion.<\/p>\n Here\u2019s a case study for HelloFresh, a provider of \u2018easy-to-cook meals in a box\u2019 for a monthly subscription fee.<\/p>\n Partnering with HelloFresh, PA platform DynamicYield launched numerous campaigns aimed at increasing customer loyalty by serving up more personalized experiences at multiple customer touchpoints. Their stated goals were to:<\/p>\n![\"marketing](\"https:\/\/www.smartinsights.com\/wp-content\/uploads\/2021\/02\/E-commerce-growth-customer-lifecycle.png\")
<\/a><\/p>\nNeed a winning marketing strategy?<\/h3>
<\/div><\/span><\/p>\nPA platforms increase your team\u2019s efficiency<\/h3>\n
Two case studies<\/h3>\n
<\/p>\nCase Study: UGG Boots product page optimization<\/h4>\n
<\/p>\nCase Study: HelloFresh customer experience optimization<\/h4>\n
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