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. Next, the team orchestrates the experience by automatically adding 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 journey analytics software, the credit card team can quickly find, analyse and deploy a solution with minimal support.
2. View All Customer Data Within the Context of Each Customer’s Experience
For many companies, data and organisational silos prevent them from interacting with customers in a way that reflects their unique context. They’re forced to rely only on the most recent interactions in a limited amount of channels to make decisions on cross-sell and up-sell offers.
A customer journey analytics solution that’s supported by a journey data hub gives you a single, unified view of the customer. It quickly integrates all of your customer 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 centres, clickstream and chat.
In addition to simply integrating data across touchpoints and internal systems, a customer journey data hub uses identity resolution to determine which events in each system are actually being performed by the same individual.
To improve cross-sell and up-sell, you can incorporate data like:
- Which products or services are being used
- If and how usage behaviour has changed
- Whether a subscription is up for renewal
- Whether the customer has called the contact centre or been on a support journey
- How the customer feels about the company and their recent experiences using Voice of the Customer feedback, like Net Promoter Score (NPS) and customer satisfaction (CSAT) survey responses
Having this data at every step along the customer journey will significantly increase the success of a cross-sell/up-sell campaign.
3. Create Dynamic Customer Segments Based on Behaviour
Behavioural segmentation is a powerful approach to drive growth and expansion through cross-sell and up-sell. Companies like Amazon use machine learning algorithms that take customer behaviour data — a user’s purchase history, items in their shopping cart, and items they’ve previously rated and liked — and make product recommendations. Consumers expect similar experiences from other retailers, as well as 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 use has changed over time, how she’s migrated among different products and which factors caused change in behaviour are all valuable in designing effective share-of-wallet strategies.
Smart-Selling
Customer behaviour 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 the customer isn’t getting enough value from products they’ve already purchased, it’s probably not a good idea to make them a fresh offer.
There are four profiles of customers you should avoid cross-selling or up-selling to as they may be unprofitable for the business:
Service Demanders. This customer segment often overuses customer service channels and tends to call support for every issue they encounter, often ignoring service announcements. When service demanders purchase more products, your support costs rise disproportionally.
Revenue Reversers. This segment gives the appearance of generating revenue, but then take it back as they’re more likely to return items, default on payments or terminate contracts early. The more they buy, the more they display such behaviour — and that costs your company time and money.
Promotion Maximisers. This segment gravitates toward steep discounts, making them unprofitable for the company overall.
Spending Limiters. This segment has a small, fixed budget that they likely won’t exceed with a company. If they buy additional products, they likely won’t increase their total spending with your company. Therefore, the money you spent in cross-selling to them wasn’t recovered as they didn’t generate any additional revenue.
To identify these unprofitable customer segments, you have to mine behaviour data and track each customer’s end-to-end journey, so you can make dynamic cross-sell and up-sell 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 up-sell offer. Input data can include the products and services that are commonly purchased and used in conjunction with one another. You can further analyse this data to identify which customers bought, as well as the dates when 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 customer support teams who can make real-time cross-sell or up-sell 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 up-selling. Hyatt used guest history and preferences from their membership program to identify guests with similar profiles and create customised 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 up-selling, Hyatt increased the average incremental room revenue post reservation by 60%, compared to similar programs in the past that didn’t use sophisticated analytics.
The hotel industry isn’t the only industry that’s rich with customer data. When stitched together throughout the customer journey, this data allows brands to move past basic audience attributes and discover more specific and relevant attributes for better engagement and personalised 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 centre, spa, food and beverage departments, etc.
In most cases, this data isn’t consolidated with the rest of the hotel’s online data from web analytics, guest satisfaction surveys and social media.
Customer journey data management platforms integrate 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 alike.
5. Execute and Measure Cross-sell and Up-Sell Campaigns in Real Time
Customers expect personalised, relevant information and offers driven by their preferences, recent interactions, and latest product and support experiences. And they’ll abandon their journeys after a single poor experience. Companies can’t afford to falter or provide a sub-par interaction at any step along the customer journey.
To improve the performance of cross-sell and up-sell campaigns, marketers must be able to make the most relevant offer to the most potentially profitable person — at the most opportune time — via the most appropriate channel.
Experience orchestration solutions enable organisations teams to do just that. Teams can activate audiences and orchestrate campaigns based on customer behaviour, such as customers who’ve converted in the past or those who’ve dropped out at a particular point in the journey.
The Full Value of Cross-Selling and Up-Selling
With the explosion in customer data collection and breakthrough technologies like machine learning and customer journey analytics, it’s the perfect time to give cross-selling and up-selling the attention and focus they deserve.
By taking a journey-based, data-driven approach, customer-centric enterprises can ensure up-sell and cross-sell offers aren’t sent to achieve only an internal marketing goal. They can be sure the offers they make will be timely and well received, so both customers and the business will more easily achieve their goals.
