Your technical team just built a new product that would allow existing users of your photo editing software to turn these pictures into music videos. Now you are tasked to cross-sell this new product. You have a list of customers who have purchased the photo editing software. You want to engage with these prospects with the hope of converting them, but you are wary of tapping into that customer database because you’re thinking to yourself:
“I have no idea where to start, there are so many names.”
“We need to cleanse the database – it’s convoluted.”
“The database doesn’t work.”
“I don’t trust the data.”
Sound familiar? Today, even small companies have big marketing databases (DB), so infrastructure becomes a lofty problem. We sat down with Michael Perla, a Principal at Symmetrics Group, to talk about approaches he has used to tackle some of these DB problems. Michael is a former VP of Strategic Marketing Planning for a Fortune 500 software firm and has spent 15 years as a consultant to some of the leading high tech firms around marketing and sales effectiveness.
In practice, there are people, process, and technology issues that need to be fixed before you can effectively use your marketing DB to look for potential cross-selling opportunities. These are three steps that Michael recommends before attempting to launch your cross-sell campaign:
Step 1: Clean and Update the Database
The first step is to make the DB cross-sell ready by cleansing and updating the data – and potentially enrich contacts with other meaningful attributes. This means performing queries to see if you have records that have been inactive for a long period of time, merging duplicate records, updating addresses and phone numbers, etc.
This may also be an opportunity to add additional information that could be leveraged for an integrated marketing campaign. You should also identify ‘must have attributes’ to determine what a complete record should look like.
If you are enriching data and bringing in third party/external data, you want to be very clear on the benefit of how you are going to be using the data. You should know how this new data will be inputed and used. (Note: Be aware that data ownership rights vary globally. Based on the country, the rules for engagement and use of data may be different. Do you know what it means to own the data?)
Step 2: Standardize and Structure the Database
The second step is to provide some standardized structure to make the DB more functional.
Standardizing the use of data fields and customer records will be essential in helping keep the DB efficient and scalable. Some basics to remember are the standard fields and ISO formats, but for further reading on effective data management, here is an Eloqua presentation on this topic.
Step 3: Provide a Process and Infrastructure to Maintain the Database
Lastly, accountability and processes need to be in place so that data coming in is clean. This means that a process and infrastructure on how to maintain the database needs to be developed and supported. This process should involve data stewards from each functional area (e.g., sales, finance, operations, etc.) that has inputs into the DB..
For example, at one company, the bookings data did not sync with the revenue data and it created focus issues. The sellers were paid on bookings and the executives were paid on revenue. The act of cross-selling ended up being less of a focus as the teams were continually fixated on revenue recognition activities, such as invoice and ship dates. The finance department began to ‘own’ the data synchronization and it helped to free-up the sales teams to better focus on cross-selling into the existing customer base.
From cleaning up the DB you’ll now be able to:
– Identify common attributes in current clients.
– Formulate a type of recommendation engine analogous to B2C recommendation algorithms (e.g., the next best customer).
– Better understand what offerings “drag” other products and services – e.g., if you buy product A, then you are likely to buy product B three months later.
– Create a cadence for ‘touching’ your contacts – e.g., how frequent, what approach, and with what vehicle.
To maximize your cross-selling success, your DB can and should be leveraged to help find trends and commonalities among your existing customers. A parallel technique to cross-selling is cross-marketing. It means leveraging your DB of prospective customers and identifying trends to market to the appropriate audience. For instance, a prospective customers tries a 30-day trial, but chose not to buy. You put them into your marketing DB and keep engaging with them in the hope of converting them.
How do you identify the prospective customers who will be receiving these emails? This is tricky because if a prospective customer does not have certain attributes and you spam them with irrelevant information, he will unsubscribe from your emailing list. There are ways you can examine your DB to reveal attributes that are strong drivers to help you identify your next best customer so you can cross-market and run targeted campaigns to stimulate interest and demand before directly cross-selling.