[Free Takeaway Inside – E-Commerce Customer Segments]
“Would you like fries to go with your burger” – McDonald’s best-known question is perhaps the classic example of cross-selling.
Amazon has attributed up to 35% of its revenue to cross-selling, through its popular “Frequently Bought Together” and “Customers Who Bought This Item Also Bought” sections.
Sephora, a beauty and lifestyle company, displays weekly rotating offers from different brands to existing customers and encourages them to customize their own bundles or packages.
There are many such examples of businesses that are leveraging cross-sell opportunities to generate profits.
One common factor that underpins the success of their cross-sell strategies is: Data.
Customer data and behavioural insights should drive cross-selling campaigns so that the recommendations are relevant and align with the customers’ needs. Data can help you tailor and personalize cross-selling propositions to customers at the right time through the right communication channels.
According to a report, data-led insights helped a financial institution increase its cross-selling efficiency by 180% in just 3 months.
The Role of Data in Cross-Selling
Cross-selling and up-selling recommendations should neither be based on intuition nor be run on a one-size-fits-all model. It should be planned based on customer behavioural data.
The most common and easily available customer data includes their demographic details such as contact information, age, gender and location. However, it’s also possible to gather other insights like individual preferences, spending habits, buying patterns and more. This type of data is incredibly important for businesses as it enables businesses to understand their customers at a deeper level and identify what they want.
Cross-selling and up-selling strategies can be improved by incorporating data like:
- Which products or services are being used
- If and how customer usage behavior has changed
- Whether their subscription is up for renewal
- Whether they have called the contact center or been on a support journey
Having this data at every step along the customer journey will increase the chances of a cross-sell/up-sell campaign manifold.
It is vital to build a unified customer view by integrating multiple sources of data across all channels. This allows you to extract accurate insights from a single source of truth.
The more data you have about your customers, the more analysis and insights you can generate. In turn, you can use those insights to drive action. This powerful data-driven approach is essential for cross-selling and recommending valuable add-ons to customers based on their previous purchase history and preferences.
Using Data Science to Drive Cross-Selling Initiatives
Here are three ways data can help you drive cross-selling and expand value for customers and businesses alike:
- Create Dynamic Customer Segments based on Behavioural Data
Behavioural segmentation goes a step further than demographical segmentation to map and build dynamic customer segments. This type of segmentation mines through data and clusters individuals into homogeneous groups based on their behavioural attributes.
Once created, cohort analysis enables the organization to identify and study profitable customer segments who are likely to make a repeat purchase. It also allows companies to personalize and customize cross-selling offers for each segment.
- Use Predictive Analysis to Provide New Product Recommendations
Understanding your customer’s previous buying behaviour can be incredibly helpful, but it can be even more useful to predict their future behaviour and actions. In order to forecast what your consumers may buy next, you need to build statistical models or regression techniques. These probabilistic models use historical data as input variables such as recency, frequency and value of previous transactions to predict what kind of products they are likely to buy in the future.
This knowledge is also valuable for marketing teams to create product and pricing bundles. Product recommendations also come in handy for customer support teams who can make real-time cross-sell suggestions based on each customer’s specific situation.
Some predictive models used in the cross-selling process include the following:
- Propensity to Buy - Predict the purchase of a product or service in a predefined time horizon in the future.
- Cross-sell Response Optimization – Maximize relevancy and response by identifying the right campaign variables for cross-selling such as offer, channel, time, creative, etc.
- Customer Lifetime Value – Calculate the expected value of the business relationship with a customer.
- Customer Journey Analysis
A critical step in finding new cross-sell opportunities is to take a journey or lifecycle-based approach. By continuously managing and measuring journeys, marketers can identify the customers' needs, interests, behaviour and goals at each stage of the lifecycle. Journey management connects the dots between customer experience and business outcomes in the following ways:
- Monitor in-journey signals or behavioural indicators to identify cross-sell opportunities
- Visualize, measure and improve customer experience throughout the lifecycle
- Orchestrate relevant and corrective actions to accelerate the journey to the next stage of the customer lifecycle
- Capture interactions across all your touchpoints to identify customers that have the highest likelihood of conversion, the next best offer for each, as well as when they’re most likely to convert
Data is the secret sauce for achieving cross-selling success. By harnessing the power of data you can insightfully allocate marketing dollars rather than running campaigns on underdeveloped research and half-baked ideas. Data can provide businesses with a much-needed competitive edge to drive cross-sell campaigns and get existing customers to buy more from you. Utilize data in your digital marketing data analytics strategy to unlock insights and maximize marketing ROI.
