Mining the Treasure Trove of Retail Analytics
For Internet retailers, analytics platforms contain the data needed to obtain all the e-commerce riches in the digital world.
When the correct data gems are excavated from these platforms, merchants gain insights into the performance of their digital stores, the satisfaction of their customers and the effectiveness of their marketing campaigns, knowledge that can be leveraged. These insights can be used to optimize business strategies and increase revenues. The problem, however, is knowing which metrics really matter the most and how they can be used to measure the success of a digital enterprise.
Consumers typically don’t arrive at your site by chance,
rather, they have seen or heard about your brand somewhere
— whether it be from friends, on social media, in
the search results or through display ads. In order to gauge
a marketing campaigns’ impact, it is vital to calculate the
cost of acquiring a customer — wherever they come from.
The cost-per-acquisition metric is determined by dividing the amount of money spent on marketing your brand (including discounts offered, the time and cost of support) by the number of customers who made purchases at your store. For example, if a merchant spends $500 on an ad campaign that brings in 10 purchasing customers, the CPA is $50.
($ amount spent on marketing) / (# of purchasing customers)= CPA
To derive a more accurate picture and gain the most value from CPA data, merchants should analyze the cost of acquiring customers in specific channels, such as social, organic search, paid search, mobile search and display campaigns. By doing this, merchants can identify the channels and campaigns that perform best, and adjust their strategies, including the financial and time resources put into each of these channels, accordingly.
Lifetime Value (LTV)
After discovering just how much it costs to acquire, the
focus should be on keeping customers rather than acquiring
new ones. In order to determine the true value
of repeat shoppers, merchants must calculate lifetime
The LTV metric not only shows how loyal a customer base is, but also the economic value of customers to a business. Founder and Chairman of MarketLive Ken Burke says that merchants should determine LTV for entire customer bases or specific customer segments. Although there are many ways to calculate the metric, Burke suggests the following formula:
(average number of purchases per year) X (average order value) X (average lifetime of a customer) = LTV
The most difficult aspect of working out this equation is defining the average lifetime of a customer segment. Because of this, many merchants assess lifetime customer value in predetermined increments, such as 12, 24 or 36 months. Burke notes that weighting factors should be incorporated into this model, such as the length of time the customer has been on file and the opportunities he has had to purchase.
If your LTV is low, it could be due to a high churn rate.
By identifying this metric, merchants can discover how
many one-time customers they have.
While this performance rate is rather easy to calculate
for subscription-based businesses, it is a bit more
complicated to generate for traditional e-commerce
models. Merchants can start by identifying a cutoff date,
so they can consider customers who have not made a
purchase in that timeframe — officially, a ‘churn’. The
best way to select a cutoff date is to take into consideration
data like a site’s repeat purchase rate. For instance, if most loyal customers make repeat purchases within
75 days, any customer who has not made a second purchase
within that period is considered “churned”. The
formula for determining churn rate:
(# of one-time customers) / (total # of customers) = Churn Rate
By identifying churned customers, or customers who are about to churn, merchants can take steps to win them back. For example, merchants can segment customer groups and email promotions to encourage purchases. Moreover, merchants who notice their churn rates increasing over time can take steps to improve customer loyalty for their digital businesses, whether it be through more personal marketing strategies or implementing more rewarding loyalty programs.
Loyalty X Factor
Perhaps the most intriguing data gem that can be derived
from analytics is the Loyalty X Factor, a mathematic
value that represents a specific metric or
combination of metrics that correlate with, and influence,
higher spending levels and lifetime value.
Essentially, the Loyalty X Factor identifies the degree of customer loyalty and what behaviors or channels influence that loyalty. Although there are many ways merchants can use analytics to compute customer loyalty, MarketLive’s Burke believes that the most straightforward way to discover a customer’s level of devotion is by quantifying their RFM pattern (recency, frequency, monetary):
(# purchases over past 12 months) X (total sales) /(months since last purchase) = Loyalty X Factor
To calculate a more robust Loyalty X Factor, Burke says merchants can weight additional events with RFM, such as the number of product categories purchased from, the number of items purchased in an order, or non-purchase metrics like page-views, cross-channel interactions, marketing and social activity. By weighting and scoring these types of events, merchants can build loyalty scores for their audiences that can help with marketing initiatives. For instance, a merchant could send a free shipping promotion to all customers within a certain score range in an effort to further influence conversions, increasing their lifetime value as a customer over time.
Digging the Data
Digging deep into data is the only way to maximize revenue and improve the performance of your digital store. All of these metrics tell a story about your digital enterprise, which is why merchants must know what data to look for and how to use it.