The pandemic forced a swift and steep evolution of digital channels in eCommerce and retail. But in the aftermath, many companies are struggling to maintain their gains. One unsuccessful approach is the “spray-and-pray” tactic of trying to recreate that pandemic momentum by pouring time, talent, money, and other resources into these digital channels. You can get a lot farther with a lot less if you are using data to derive actionable insights and investing in the right resources to help you personalize at scale.
How do you identify actionable insights?
In order to avoid analysis paralysis, data insights should be actionable, impactful, and insightful. Here are a few key attributes of actionable insights:
- Relevance. An insight must be relevant to your goals in order to inspire action. Insights that are tied to key business objectives inspire more urgency in whoever is held accountable for them.
- Context. Context gives data meaning relative to other numbers and statistics. It’s hard to move forward with an actionable insight without knowing what makes it uniquely interesting and important to your business.
- Specificity. The more specific an insight is, the more likely it is that you are able to act on it. Actionable insights should be impactful and light a specific way forward to reach your business goals.
What is an example of an actionable insight?
An actionable insight starts with a goal in mind. Let’s say you work for a beauty brand. One of your quarterly goals is to increase the amount of revenue attributed to email conversions. So you launch a number of personalized emails that include discounts, limited-time promotions, and relevant company news.
This kind of goal-oriented personalization can help you increase conversions from email by 25% MoM and can have a direct impact on your revenue goals. It’s important to remember that this type of simple personalization is the first step you’ll take toward achieving your revenue goal, but by no means is it the last. As a data-driven marketer, your next step is to replicate these results and scale.
In this beauty brand example, you do some digging and find out that this increase in conversions is primarily from one promotion that entices consumers to take a quiz and build their own hair care regimen. This type of personalization is a clever and lucrative way to gain more first-party data while engaging your customers and putting them in the driver’s seat. Now that you’ve given context to the data, you have illuminated a possible way to move closer to your goals. With more specific data, you can decide how to more prominently feature the regimen quiz on your site.
The role of AI in Data-Driven Marketing Campaigns
Marketing teams and data scientists often struggle to identify actionable insights at scale. The process of sifting through data is usually too manual and takes a long time. And once you’ve finally identified a creative way to move one group of consumers closer to conversion, you will likely find that the same tactics won’t work on other segments or individuals. How do you scale personalization efforts without pouring endless resources into more personnel?
This is where artificial intelligence can come into play to create great customer experiences with monetary impact. In a survey of CEOs in the United States, 31% of respondents said that “difficulty making quick technology-related decisions” was their top challenge of digital transformation. An AI marketing solution can quickly make recommendations for personalization initiatives based on actionable insights from consumer data, ultimately increasing marketing ROI.
For example, let’s say that your goal is to increase conversions from your worldwide audience. An AI personalization engine could notice that your website has a high bounce rate from visitors in Latin America. Based on this information, the AI could recommend content that is written in Spanish, and even help your visitors change their preferred browsing language—all within a few seconds of uncovering the insight.
According to data from Forrester, 74% of companies say they want to be “data-driven,” but only 29% say they are good at connecting analytics to action. As brands strive to do more with less, it’s important to not only collect data but also make data-driven decisions to increase revenue and optimize your customer experience.