Letting Your IoT Customer Data Work For You
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As eCommerce continues to grow, retailers are seeing more competitive pressure. Whether it is keeping up with product quality, delivery expectations, or margins, eCommerce is becoming more challenging. With all of these different problems, you might feel overwhelmed. The solution? Focus on business fundamentals like margins and pricing.
In eCommerce 1.0, low prices were a key advantage in attracting market share. Consumers assumed that some eCommerce retailers had lower operating costs so those savings should be passed on to them. There is some truth to that. Amazon does not have to pay for expensive retail space in large cities. However, operating a large scale eCommerce operation requires considerable supply chain expenses.
In many cases, eCommerce companies are struggling to maintain acceptable profit margins to achieve their goals. In 2014, Marketing Sherpa found that gross eCommerce margins were 30% and higher based on a survey of 413 companies. In 2018, these higher margins continued but required more effort. Private and manufacturing-based eCommerce models were growing much faster than drop shipping. Overseeing manufacturing processes introduces more complexity. If this complexity is not well managed, customer satisfaction and purchase behavior may fall.
Implementing dynamic pricing is one of the most potent levers to pull to enhance eCommerce performance. Instead of waiting for the month-end to adjust pricing, use data tools to adjust prices rapidly in response to the market. To support effective dynamic pricing, use these three data strategies.
Larger eCommerce companies have customer data in multiple systems. One application has marketing insight to help you understand marketing engagement, social media analysis, and beyond. Another system will have data on order volume, order frequency, and shipping. Manually integrating all of these data sets is a huge pain. However, it is critical to understand customer behavior.
To build a comprehensive customer profile, clarify what you are aiming to achieve first. Here are a few guiding principles to inform your 360 customer profile development.
Once your customer data profiles are created, there are two ways to implement dynamic pricing.
Let’s take a closer look at applying dynamic pricing to your customers. For illustration, let’s assume that you identify two segments. Segment one is high order volume customers (e.g., ten orders over twelve months). Segment two is infrequent customers (e.g., buyers who wait for holiday promotions). By machine learning, you can develop multiple pricing scenarios, such as the following.
Segment 1: Create a one day, offer 10% price discount as a loyalty reward to maintain customer loyalty.
Segment 2: Develop a “reactivation” pricing promotion to encourage customers to buy more frequently.
With machine learning, you can develop pricing change experiments hundreds of times per year. That increases the chances you will find profitable price points without working around the clock.
Predicting the future, especially for seasonal activities, is crucial to success in eCommerce. For example, many eCommerce companies begin ordering inventory in June for the year-end customer. If inventory can be optimized, your company saves money in terms of warehousing cost and financing. Here are a few ways to introduce the power of behavioral analytics in eCommerce.
You don’t need to start from scratch with dynamic pricing. There is a way to shortcut the process, find the right data, and design pricing experiments. How is all this possible? Contact Blue Orange Digital to improve your pricing strategy. In a matter of weeks, you could improve your margins by 5-10% solely by leveraging dynamic pricing. Find out what’s possible for your company by contacting us now.