3 Types of Analyses Every Brand Needs to Do
Gone are the days of making decisions on gut feel. With technological advances and an ever-increasingly competitive marketplace, companies can no longer afford to make uncalculated decisions. A new level of analysis is necessary to succeed. Analyses conducted typically fall under the following categories: customer, product, and marketing campaign effectiveness.
Customer analysis
This type of analysis focuses on your customers’ buying behaviours. The goal is to provide insights on who are your customers, what are they buying, and the value that they hold for your brand.
Assessing customer value bands by assigning value and priority to customers based on how much they spend on your products, how often they purchase, etc. is an important first step. Customer value bands create an easy heuristic in which brands bucket their customers based on a set of rules, resulting in an overarching ranking of how valuable each customer is to the business.
Churn analysis is an important piece to look at for any company, whether it’s selling products or services. It is imperative to know what customers are churning out, after how much time/how many orders, and know what are the indicators for a customer that may churn. An increasing number of professionals are of the mindset that customer retention is more valuable than acquisition, but too few brands place enough importance on retention and renewal (in the case of subscription services) due to the advanced level of analysis that is required. Customer retention is much cheaper than acquisition, and is a very easy way to improve profitability!
Based on your data, other analyses can be completed, too, but the above are two examples that can be applied to any industry.
Product analysis
A product analysis will provide insights on how each product is performing, whether certain products are most popular with a specific customer group and/or in a specific region, and whether — based on the data available — the prices are resonating with customer.
An example is basket analysis; what products are typically sold with one another. It’s great for determining whether certain products can be bundled or cross-sold for a higher share of wallet. Over one third of Amazon’s sales are a direct result of their cross-sell recommendations. Though they require and advanced recommendation engine due to the breadth of their product offering, most brands can achieve similar results by simply understanding what customers buy with what other products, and offering them incentives (typically in the form of discounts) to do so.
Marketing Campaigns
The goal with this analysis is to determine whether the marketing campaigns being conducted are effective, and if so, to what degree. On some channels it’s easier to attribute dollars earned to dollars spent. On others, it’s a little less straight-forward. You can understand your campaign effectiveness (at a high level) by applying a decay rate to the effectiveness of marketing activities, as calculated by the number of purchases made by each customer who received the marketing communication. You can apply a decay factor of 1/2 for every week that elapsed after sending out the email/text/posting the ad. This way the purchases made will be attributed in part to a number of deployments of your campaign, and you avoid false attribution (because let’s face it: one single email is unlikely to drive all the sales that some in within the following week… it’s a compound effect of all your marketing efforts).
