Boosting conversion rate in e-commerce: three proven data strategies and how to prioritize them
Data science is powerful for quickly identifying conversion problems and finding effective solutions. Here’s how you get the most out of it.
Conversion rate is always a focus for e-commerce brands and is especially important now. During good times, brands may prioritize growth at all costs and tolerate some low-quality traffic and drop-offs. However, in a challenging economy, brands scrutinize all costs, big and small.
As a brand owner or operator, you are likely striving to improve your conversion rate. Quickly and cost-efficiently solving the conversion puzzle can help your brand survive and thrive in today’s uncertain environment.
How can you achieve your conversion goal? As discussed in A complete guide to data-driven customer acquisition, there are three proven data-driven strategies on a high level:
- Find the right marketing channels and campaigns to acquire customers.
- Invest in customers with the highest lifetime value (LTV).
- Analyze customer segments to personalize your marketing initiatives.
Each strategy requires upfront input, in this article, we’ll discuss how you can prioritize your conversion optimization efforts to get the best return on investment within your constraints.
Map your shopper journey
Just as Google Maps guides our travels, shopper journey maps can help e-commerce brands gain a clearer understanding of shopper interactions and make better decisions to increase conversion rates. The graph below demonstrates a simplified e-commerce shopper journey.
Drop-offs at each touchpoint of the shopper journey will hurt the conversion rate. To quickly improve conversion rates, we need to identify the most impactful drop-offs which present the biggest growth opportunities.
Locate your biggest growth opportunities
A brand’s business model largely influences the scale of drop-offs at each stage of the shopper journey. Before analyzing data to find growth opportunities, let’s look at the key factors related to conversion rate.
Product type
When, where, and how consumers buy a product can indicate at what point they may drop off. Traditional e-commerce metrics, such as the sales cycle, average order value, and purchase frequency, only tell a partial story about a brand. Consolidating these metrics is where the magic happens. It instantly brings a brand’s characteristics to mind.
For example, impulse-buying products include low-to-medium value snacks, apparel, cosmetics, electronic devices, etc. Industry data suggests that ideal customer profiles (ICPs) typically become aware of these products and make a purchase within a week. Sales cycles are often as short as a day.
Cutting back on low-return paid campaigns and fine-tuning campaigns to make them profitable is the most effective strategy for marketing impulse-buy products. In fact, many brands in this category can increase their conversion rates by over 20% in a short time by applying this strategy alone.
Subscription model
The subscription model has become increasingly popular in e-commerce over the past few years. Its most significant advantage is that subscription brands’ revenue mainly comes from subscribers, who are repeat customers. As a result, customer acquisition costs can be lower in the long run.
For subscription brands, attracting shoppers who are likely to become repeat customers is critical. The best strategy is to double down on the most valuable customers. Brands can do this by calculating each customer’s lifetime value, analyzing their characteristics, identifying their traffic sources, and finding customers who look like those with high lifetime value.
Company stage
While trying all three data strategies at once may be tempting, a data strategy will only bring marginal returns if it outpaces a brand’s development. For example, it will be challenging for an early-stage brand to tailor marketing campaigns by customer segments. Technically, there won’t be enough customer data to implement the strategy; economically, the effort required is often beyond what the brand can afford.
At the early stage, brands benefit the most from brand-level conversion optimization. As brands mature, opportunities emerge for a product or persona-level conversion optimization.
Align data strategy with your profitability goals
After brands identify their biggest growth opportunities, the next step is to find the right solutions to unlock them. In recent years, analytics tools have emerged, giving marketing and data teams more options to solve data problems. But how can you pick the tools that best fit your business case? Here are a few things to keep in mind.
First and foremost, the return on investment for a data strategy must be measured in terms of revenue increase or cost savings. While comprehensive analytics dashboards can be delightful, they can also distract executives from making business decisions. When evaluating an analytics tool or in-house solution, always ask how it can help improve your bottom line.
Secondly, it is important to remember that any tool comes with a cost. In addition to the cost of the software itself, implementation and software training will require internal resources. Furthermore, it is common that a tool may not completely fit your use cases, requiring additional internal custom development, which comes with an additional cost.
Finally, many brands may not realize that it takes time and human resources to make sense of their data. The more extensive data analytics a brand conducts, the higher the costs it needs to bear. Therefore, it’s essential for brands to stay focused and only invest in their largest growth opportunities.
Data science is a powerful tool for quickly identifying conversion problems and finding effective solutions. To get the most out of it, brands should keep their profitability goals in mind and carefully assess the return on investment for each problem and its associated solutions.
If you have any questions about this topic or would like advice on your specific business case, feel free to contact me on LinkedIn or newsletter@ivyliu.io.
To learn more about using data science to level up your business and optimize your marketing, follow me on LinkedIn or Medium. Until next time.
