By Swati Sahai
“Would you like fries with that?”— McDonald’s classic question is perhaps the best-known example of cross-selling.
Amazon has attributed up to 35% of its revenue to cross-sell, both through its “Frequently Bought Together” and “Customers Who Bought This Item Also Bought” features.
JetBlue has in the past made as much as an additional $140million in revenue through its upsell program called ‘Even More Space.’
Meanwhile, Lufthansa uses VR glasses to entice travelers into last-minute upgrades to ‘Premium Economy’ at the boarding gate and acknowledges that this successful upsell program has brought in significant ancillary revenue.
There are many such examples of major enterprises that are using upsell and cross-sell opportunities to generate profits. Yet marketing budgets continue to focus heavily on new customer acquisition at the expense of driving upsell/cross-sell and retention of existing customers.
In this post, I take a detailed look at:
- What is upsell and cross-sell? Are they the same?
- Why are upselling and cross-selling important?
- 5 steps to find better upsell and cross-sell opportunities using journey analytics
Upsell Or Cross-sell? Are They the Same?
Upsell and cross-sell are often confused and used interchangeably, but there is a meaningful difference between the two.
Upsell is when you encourage your customer to buy a higher priced alternative of the current consideration. Upselling encourages customers to purchase a more expensive model in the same product or service family, or to augment the original purchase with additional features such as warranties.
A familiar example is when retailers offer consumers to add a protection plan to their purchase, as shown below.
Cross-sell is when you recommend a product that complements your customer’s existing purchase, but is from a different category. In this case, the retailer in the previous example offers a complementary product to the one already chosen.
Cross-selling identifies products and services that satisfy additional, complementary needs that are unfulfilled by the original purchase. A common example in retail banking is when your bank offers you a credit card after you open a new savings account.
Cross-selling and upselling are closely related because they both focus on providing additional value to customers, rather than limiting them to products they have already considered or purchased. The key to success in both is to understand what your customers value most and then respond with products and services that truly meet those needs at the right time and through the optimal channel.
Why are Upselling and Cross-Selling Important?
1. Generates Additional Revenue More Efficiently Than Selling to New Customers
Lead generation is expensive. It is far easier and more profitable to upsell or cross-sell an existing customer than to make a new sale to a brand new customer.
A SaaS benchmarking survey looked at the customer acquisition costs (CAC) needed to acquire $1 annual contract value (ACV) for new customers compared to upsell to existing customers. The results showed that the median CAC for $1 of new ACV was $1.18. This means that it would take a company more than a year to earn back the cost of acquiring new customers.
The median CAC per $1 of upsells was only $0.28, about 24% of the cost to acquire each new customer dollar. The payback period for upsell revenue is only about a quarter, far less than that of new customers.
2. Builds Stronger Customer Relationships
Upselling and cross-selling is not just a sales and marketing tactic to make more money for a company. When done right, it helps a customer derive more value out of their purchases, do their jobs better and make their lives easier. It generates more opportunities to provide good customer experiences and build deeper, stronger customer relationships.
3. Leads to increased Customer Lifetime Value (CLV)
Upselling and cross-selling are great ways to increase your customers’ profitability over time and keep them coming back for more.
Discover Upsell Opportunities with Customer Journey Analytics
A leading hotel and resort company uses Pointillist to identify customers who are likely to convert on upsell offers.
The marketing team at a national hospitality leader is tasked with increasing upsell conversions. They collaborate with their analytics team, who leverages the Pointillist Customer Journey Analytics platform to understand cross-channel behavior and identify leading indicators of customers who are more likely to convert on an upsell offer.
The team analyzes customer journeys to understand why customers transitioned. They find that that adding a loyalty program point bonus increases their upsell rate from 9% to 23% compared to their standard dining and spa packages.
The team also analyzes upgrade rates based on a variety of customer attributes, including age and the number of stays per year.
They share this information with the marketing team and set up a trigger, so that future customers with this behavior will be automatically presented with the best upsell offer when they book their reservation.
5 Steps to Find Better Upsell and Cross-sell Opportunities Using Journey Analytics
1. Take a Journey-Based Approach
Bain & Company recently analyzed the telecommunication industry and found that nearly 60% of customers split their mobile, internet, TV and landline services across multiple providers. Convincing just 10% of those customers to switch even one service away from a competitor, could mean an incremental revenue of $480 million for the telco.
This is a huge opportunity. To execute on it, CX leaders are reorganizing their business around journeys and adopting sophisticated approaches like customer journey management.
Indeed, the first and most important step in finding new upsell and cross-sell opportunities is to take a journey-based approach. By continuously managing and measuring journeys, teams across the enterprise can better understand the customer’s likes, interests, behavior and most importantly, their goals.
Many companies are challenged by data and organizational silos, which prevent them from interacting with customers in a way that reflects their unique context. They are forced to rely only on the most recent interactions in a limited amount of channels to make decisions on cross-sell and upsell offers.
Customer journey analytics is a breakthrough technology that provides the power to look across millions of complete journeys connecting multiple touchpoints over different channels and time periods. These solutions enables enterprises to orchestrate actions like upsell or cross-sell offers only when it is relevant to a customer and helps fulfill their goal.
Using a journey-based approach enables a leading retail bank to increase credit card conversions in the following journey analytics use case.
Customer Journey Analytics Helps a Major Retail Bank Increase Credit Card Conversions
The credit card team at a major retail bank is tasked with improving credit card opening rates among millennials. To understand the role that different channels play in credit card offers and their respective efficiencies, the bank uses the Pointillist Customer Journey Analytics platform.
They uncover a variety of customer journeys across online and offline channels—such as branch visits, website browsing, mobile data, email data and in-app interactions—that lead customers to view a credit card offer. Within minutes, they discover how many customers go on to apply for a card online versus how many reject or ignore the offer. With one click, they are able to see how many customers move forward at each step, how many drop out and how many are still present at that step.
Using Pointillist, the bank determines that the offer converts better for people who see it as an email than as a text message or within the bank’s mobile app. Based on this information, they decide to send a personalized email offer to those who view the credit card offer and then abandon their journey.
A few days later, the credit card team reviews the results and are delighted to see a large number of the email offers have been converted into new credit card applications. Since Pointillist is already integrated with their email platform, they decide to set up an automated trigger to add anyone abandoning this journey in the future to the new email campaign.
This multichannel analysis would have taken days and consumed high-level data science resources to accomplish using traditional analytics approaches. Using Pointillist, the credit card team is able to quickly find, deploy and analyze a solution themselves with minimal outside support.
2. Build an Omnichannel Customer View
Your customers engage with different touchpoints throughout their journeys and likely take multiple journeys concurrently. If marketers want to make cross-sell and upsell offers that will have a high win-rate, even as customers hop across channels and devices, they must have a complete, single customer view across all their touchpoints.
A customer journey analytics platform that is supported by a journey data hub enables you to get a single, unified view of the customer by quickly integrating data across a variety of systems and channels such as point-of-sale systems, email marketing platforms, marketing automation systems, data warehouses, websites, surveys, call centers, clickstream, chat, etc.
In addition to simply integrating data across touchpoints and internal systems, customer journey analytics solutions use identity matching to determine which events in each system are actually being performed by the same individual.
Cross-sell and upsell can be improved by incorporating data like:
- Which products or services are being used
- If and how their usage behavior has changed
- Whether their subscription is up for renewal
- Whether they have called the contact center or been on a support journey
- And much more
Having this data at every step along the customer journey will increase the chances of a cross-sell/upsell campaign manifold.
3. Create Dynamic Customer Segments Based on Behavior
Behavioral segmentation is a powerful approach to drive growth and expansion through cross-sell and upsell. Companies like Amazon employ machine learning algorithms that use customer behavior data—such as a user’s purchase history, items in their shopping cart, and items they’ve previously rated and liked—to make product recommendations. Consumers expect similar experiences from their telecom providers, banks and even health insurers.
Dynamic customer profiles
Internal and external data that spans several years can be combined to build dynamic customer profiles. Knowing how the customer’s product usage has changed over time, how she has migrated among different products and which factors caused change in behavior are all valuable in designing effective share-of-wallet strategies.
Customer behavior data can also be used to determine when not to target some customers for an offer. For instance, if a customer has had a recent negative experience with your company or are not getting enough value from products they’ve already purchased, it is probably not a good idea to make them a fresh offer.
A Harvard Business Review article identifies four profiles of customers you should avoid cross-selling or upselling to as they may in fact be unprofitable for the business:
- Service Demanders. This customer segment often overuses customer service channels and tends to call support for every issue that they encounter, often ignoring service announcements. When service demanders purchase more products, your support costs rise disproportionally.
- Revenue Reversers. Revenue reversers give the appearance of generating revenue, but then take it back as they are more likely to return items, default on payments or terminate contracts early. The more they buy, the more they display such behavior, costing your company time and money.
- Promotion Maximizers. This segment gravitates towards steep discounts, making them unprofitable for the company overall.
- Spending Limiters. Spending limiters have a small, fixed budget that they will not exceed with a company. If they buy additional products, they will not increase their total spending with your company. Therefore the money you spent in cross-selling to them was not recovered as they did not generate any additional revenue.
In order to identify these unprofitable customer segments, you have to mine behavior data and track each customer’s end-to-end journey, so you can make dynamic cross-sell and upsell decisions that are efficient and profitable in the long run.
4. Use Predictive Analytics to Provide New Product Recommendations
Customer journey data can be used to predict each customer’s likelihood of responding to a cross-sell or upsell offer. Input data can include the products and/or services that are commonly purchased and used in conjunction with one another. This data can be further analyzed to identify which customers bought, as well as the dates when the purchases were made.
This knowledge is valuable for the product marketing team to create product and pricing bundles. These product recommendations are also valuable to provide to customer support teams, so they can make real-time cross-sell or upsell suggestions based on each customer’s specific situation.
Hyatt Hotels Uses Predictive Analytics to Boost Revenue
Hyatt Hotels has aligned its operations across 500 hotels globally to use predictive analytics for cross-selling and upselling.
Hyatt used guest history and preferences from their membership program to identify guests with similar profiles and create customized offers for each based on a unique combination of amenities, room upgrade, or activity packages. The program worked by prompting front desk agents with relevant and timely messages such as, “Based on what we know, this person will want a room with an ocean view” or “This person will likely be looking for a spa package.”
Using predictive analytics for cross-selling and upselling, Hyatt was able to increase the average incremental room revenue post reservation by 60%, compared to similar programs in the past that did not use sophisticated analytics.
The hotel industry, in general, is rich with raw data, which when stitched together throughout the customer journey, allows marketers to move past basic audience attributes and discover more specific and relevant attributes for better engagement and personalized service offerings.
The most common barrier to getting and implementing real-time insights is that the data lives in silos in disparate systems such as property management system (PMS), point-of-sale (POS), central reservation system (CRS), call center, spa, food and beverage department etc. In most cases, this data does not get consolidated with the rest of the hotel’s online data from web analytics, guest satisfaction surveys and social media.
Customer journey analytics platforms can solve this problem by integrating data from different sources into one central platform that sits as an intelligence layer with existing technology stack. In this way, customer journey analytics generates insights for marketers and customer experience teams.
5. Efficiently Execute and Measure Cross-sell and Upsell Campaigns in Real Time
Customers today expect personalized, relevant information and offers driven by their preferences, recent interactions and latest product and support experiences. They are ready to abandon their journeys with a single poor experience. Companies cannot afford to falter or even provide a sub-par interaction at any step along the entire customer journey.
To improve the performance of cross-sell and upsell campaigns, marketers need to be able to make the most relevant offer to the most potentially profitable person, at the most opportune time, via the most appropriate channel.
Customer journey analytics enables organizations teams to do just that. Sophisticated platforms enable teams to activate audiences and orchestrate campaigns based on customer behavior, such as customers who have converted in the past or even those who dropped out at a particular point in their journey.
Advanced customer journey analytics platforms integrate with commonly used marketing tools, so you can engage with your customers using your existing technology stack. The result is a new level of performance for marketing and CX campaigns through significantly better precision, targeting and timing.
Finally, to gauge cross-sell and upsell campaign effectiveness and determine the need for mid-stream adjustments or alterations to future campaigns, you will need to monitor insights in real time and over the long term through interactive dashboards.
To Sum It Up
With the explosion in customer data collection and breakthrough technologies like machine learning and customer journey analytics, the time is now ripe to give cross-selling and upselling the attention and focus they deserve.
By taking a journey-based, data-driven approach, customer-centric enterprises can ensure that upsell and cross-sell offers are not sent to merely achieve a marketing goal, but to help customers achieve their own goal, whether that is purchasing a new credit card or upgrading their cable package.