如何将数据转化为可操作的洞察 | Elastic_AI阅读总结 — 包阅AI

The last step in turning data into actionable insights is analysis and retrieval. This means figuring out what the patterns and trends you’ve spotted mean for your business and how you can use them to make more informed decisions.

For example, say your ecommerce store sells two different brands of canned tomatoes. The marketing team contacts you and asks how tomato brand A is doing because it recently got some online attention when it was featured in a spaghetti recipe by a famous chef. After examining the data, you discover that traffic to brand A’s page has spiked: it has twice the amount of traffic but a lower conversion rate than tomato brand B, which has less traffic but a higher conversion rate. However, your month-over-month data shows that overall sales for brand A are up even though the conversion rate is down. Brand B, which has a lower price, has historically had a higher conversion rate which remains steady month over month.

What would this data look like if it was turned into actionable insights?

Simply presenting the conversion rates isn’t the full picture. You want to compare the completed purchases, month-over-month trends, and profit margins of the two brands. You might want to dig deeper and see if people purchasing brand A are new customers or if there are any other items they’re also adding to their carts. (Yes, many are new customers. And, oh look, they’re also buying the same brand of spaghetti the famous chef recommended.)

Finding insights in the data means you have:

• Identified key findings: You spotted significant patterns and/or anomalies in your data that align with your business objectives. For example, Brand A sales are up without affecting the sales of Brand B, and spaghetti sales are up, too. That’s good!

• Contextualized the insights: You and the marketing team communicated about why this anomaly might be happening in the first place, which allowed for better context.

• Prioritized the findings: Not all insights are equally important. For example, brand B’s consistent sales are good to note but don’t need to be focused on.

• Continued monitoring: These insights aren’t static — they will evolve as new data is continuously generated, which you can derive insights from later.

This way, your data has given marketing the information they need to take action. They’ll spend the next month promoting brand A and the spaghetti to try to boost sales. Meanwhile, you’ll continue to monitor the data and see if their promotional campaign was helpful while also looking for any new patterns or anomalies along the way.

Turning data into actionable insights doesn’t have to be intimidating. By following a few key steps — gathering and cleaning your data, analyzing and visualizing it, and then highlighting insights — you can enable your colleagues and stakeholders to make informed decisions that help your organization reach its goals.