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3 Types of Data You Can Use to Increase Profits
Posted on April 9th 2014
I think of data scientists as artists; each silo of data is a different jar of paint adding to the beauty of the final piece. Instead of seeing an excel file filed with pages of numbers, they see potential to pull together something striking by finding patterns that blend well together. Just as the artist’s curiosity led her to her masterpiece, the data scientist’s inquisitiveness will lead the company to deeper, strategic insights used to make changes and increase profit.
While Forbes predicts it may be a while before companies see profit from big data, it is refreshing to know that it is not due to a lack of technology, but a corporate mind-shift in workplace culture moving toward data-driven thinking. Companies need to move past trusting their gut and into trusting their data. When you think about the mass amounts of data companies own on customers (transactional, behavioral, demographic, etc.), coupled with company data (inventory, real-time sales history, logistics, web activity, etc.), there is a large opportunity to decrease bottlenecks, while increasing loyalty, efficiency and profit along the way.
While there are infinite amounts of data that can be analyzed to help add to your bottom line, here are three examples help get you in the right mindset:
1. Customer Data –The phrase “customer is key” comes to mind here as knowing your customer and their preferences will put you leaps and bounds above the competition. There are countless ways to start to look at new patterns, but a good place to start is to identify the data you have and start to ask questions. For example, you might notice that sales spiked 3x its normal range during April and May, which your data later reveals is because of a promotion you ran with an 84% coupon redemption rate. With the implementation of a DMP, you can begin to tie this information to an individual user’s profile, create their shopping persona and market to their unique personal needs. Suddenly, you realize Sally visits your website every Monday at 8:45AM (searching for accessories), but never buys, which in turn might prompt you to send her an e-mail at 8:30AM with a 20% off coupon for accessories.
2. Employee Data – When retailers think of profit, they often jump to sales data as a primary source for success measurement. But it is important to look at your employee data to see what the data reveals. For example, you may have thirty people in the company who work on processing orders and you discover the process is inefficient, causing delays and time for an overhaul. By collaborating with IT you are able to develop a new workflow management program to cut the order processing time from six steps to three steps. This time-savings allows your company to reallocate the time of these employees to new, profit-generating activities, while still exceeding historical efficiency levels.
3. Supply Chain Data – Logistics can be complicated for any company. Knowing how much of a product to have on-hand can vary based on historical, seasonal and trending analytics. Along the process to delivery, there can be many delays, hold-ups or antiquated ways of performing tasks not rooted in data-driven thinking. Analyzing the path to purchase to delivery is a large undertaking, but can reap bountiful rewards. Once the product is delivered (if the shopper selects online delivery), you can send a follow-up survey requesting information on their experience. This information can be imported into their profile for future reference as your team develops resolutions to alert them once a viable solution is in place to their concerns. For example, if your system is not up-to-date with inventory, a customer could potentially order a product with overnight shipping expecting it in 1-2 days. Not only is the product not available, but now both the company and customer have the added headache of refunding the overnight shipping (and potentially the product if it was a time-sensitive purchase), when this problem could have been avoided in the first place.
As you delve into the depths of data analysis, remember the key is to discover the data you need to do your best analysis. Be sure to visit one of our previous blog posts, 3 Ways to Gather Your Data Resources, for more insight on data to include in your DMP.