- 1 Turn customer data into customer intelligence.
Turn customer data into customer intelligence.
You know you’re supposed to use data to improve your marketing, but sometimes your customer intelligence doesn’t feel very intelligent.
Data has changed the marketing game in recent years. You’ve experienced it firsthand. Technology provides a veritable flood of customer data coming into your business daily from multiple channels and it would be foolish — if not completely insane — to ignore it. In fact, while data used to be a significant competitive advantage for early adopters of marketing analytics technology, it’s basically par for the course now.
But that doesn’t mean all companies get the same value from their data. From my vantage point, some brands are able to use their data to paint a beautifully complete portrait of their customer. Others are still drawing stick figures. The businesses that are ahead of the curve are the ones that have aligned their internal teams, processes and technologies to capture and draw actionable insights from all of their data.
While the need is obvious, best practices for managing customer data can be elusive. Wherever you are on that path, here are a few recommendations to set up your organisation for customer intelligence success:
Narrow your focus for the best customer intelligence.
With the deluge of data continually pouring into your business, it can be tempting to think you need to centralise and utilise every piece of customer info. I am a big believer in big data, but that might not be the best idea for at least two reasons:
First, it may make your life harder, not easier. The idea of having all of your customer info in a data lake is compelling. But in reality, it can turn the process of retrieving your data into a nightmare, making it even harder to work with and analyse. Digital marketers like you need capabilities that allow you to iterate through analytical thought processes very quickly. Adobe Analytics Cloud balances the tradeoff between flexibility and speed to insight — even for big data.
Second, even if you do have the capabilities and skill set to analyse colossal datasets, it probably isn’t necessary — or optimal — to use every piece of data you own. If this was your goal, you’d end up being overly reliant on your data science team. The problem with big data is that it can quickly become too big. Your role is to understand your customer. You probably don’t need supply chain data or HR data to do that. Instead, look at each of the data sources that could potentially tell you something meaningful and figure out which fields are actually relevant. Keeping your efforts more focused will allow you to fully leverage the skills of your marketing analysts and use your data to create better experiences for your customers.
But how do you know which data is relevant? Look to your customer journeys. A good understanding of your customer journeys helps you to know which data points are going to affect your marketing and lead to better customer experiences. The better your customer journeys, the better your data models can be.
Hadoop and similar big data technologies, will continue to play a major role in the growth of customer intelligence efforts. But there is a need for another layer of analytics that provides both the speed and flexibility marketing analysts will need in order to create better customer experiences. That’s where Adobe Analytics Cloud shines.
I also want to point out that narrowing your customer intelligence sources can help with your data hygiene efforts. Once you have determined which data sources are most relevant, you can focus your efforts on ensuring the integrity of those sources. Start by working to integrate the two most relevant sources and build from there. It’s much easier to control a few crucial data sources than to worry about suspect data from countless places.
Jumpstart your customer intelligence implementation.
Recently, I was asked, “If you were running a business, what would you do to ensure success when implementing a Customer Intelligence solution?” I answered with these three things I would do to make the most of the implementation:
1. Join the Adobe Device Co-op.
Marketing to people as they hop from one device to another — from desktop to smartphone to tablet — is a huge challenge. Having a customer ID is key to good customer intelligence because, without a view of the customer that you can tie across channels and devices, it’s difficult to personalise experiences or optimise your marketing mix. The Adobe Device Co-op is the best thing that exists to do that. Some of the biggest brands in the world have joined the Co-op, creating a network of 1.3 billion known devices, which allows Co-op members to track consumers as they move between devices. Leveraging this data network gives you a clearer cross channel picture of your customer, allows you to retarget advertisements across devices and lets you personalise content based on the information you have about the person, not just the device they are using. Starting here is really a no-brainer.
Learn more about the Adobe Device Co-op. http://www.adobe.com/in/enterprise/cloud-platform/people.html#devicegraph
2. Leverage data from multiple functions.
I’m sure it’s no surprise to you that other departments in your organisation have data that is useful to your marketing efforts. This is especially true of CRM data, which is probably the best complement to the data you’ll have in Adobe Analytics Cloud. Find away to leverage all of this other data. Often, this may require you to go to individual silos with a value exchange. What can you provide them in exchange for the data you want? I know that sounds like a nod to corporate politics, but it’s the reality many organisations operate in.
Let’s use CRM as an example of what this exchange might look like. CRM analysts have volumes of great descriptive and transactional data that could help you build out your audience profiles. What they lack is the contextual behavioural data that you own. They might have a list of purchases a customer has made, but don’t have a view of all the products they looked at before purchase. Or, they know that someone closed their account, but can’t see when they visited the “How do I cancel my account?” web page before calling to cancel. Having this data could help them work smarter with their contacts. Brokering a value exchange allows both teams to benefit from more customer visibility and can give a big boost to your customer intelligence.
3. Create data partnerships.
Second-party data is often an underutilised source of customer intelligence. It comes from a strategic partnership with an organisation that has first-party data that is complementary to your own. Creating data partnerships with related (but noncompetitive) organisations can be a great way to add additional colour to your audience profiles and give you competitive differentiation. For example, an airline has data about rewards members and knows who their business travellers are. This information may be valuable to a credit card company that wants to market a travel rewards card to business travellers. If the two companies form a partnership, they can share data in a way that is beneficial to both. In the data economy we are living in, partnerships can help you to obtain access to proprietary data that might be just what you need to gain an edge over the competition.
Look for the right people to set up your team for success.
As digital marketing teams mature and start to gather more customer information from more sources, they will likely run into a gap between the potential of customer analytics and the skillsets necessary to make it actionable. Data scientists are seen as the solution, but it can be very difficult to find someone with the right mix of analytics smarts, flexibility and business savvy to fill that spot on your organisation chart. Rob Bearden, CEO of open-source data platform, Hortonworks, offered this bleak assessment of the data science talent market, “[finding] truly qualified data scientists … may be the biggest imbalance of supply and demand I’ve ever seen. The talent pool is, at best, probably 20 per cent of the demand.”
So, what do you do when you aren’t lucky enough to secure an in-house data scientist? Hire people who think like data scientists and then use Adobe’s tools to help fill the gaps. We work every day at creating solutions to help marketing analysts produce the insights that currently require a data scientist. But if you’re wondering what it means to hire someone who “thinks like a data scientist,” here are a few traits I would look for when building your team:
1. An understanding of how to bring together disparate data sources.
With the benefit of Adobe Analytics Cloud, you don’t necessarily need someone with all the technical chops of a data scientist, but you do need someone who understands which data sources fit together and why. With the volumes of data you have from countless sources, it can feel like someone dumped a 1,000-piece puzzle in front of you and then took away the box. You need a person who knows which piece goes where and can help you to assemble the full picture of your customer. As I discussed above, understanding the customer journey can help you to know which pieces of the puzzle are most important. The right person will know which data sources complement each other and how to fit them together.
2. A healthy measure of scepticism.
This may be the most important trait. You need people who recognise the limitations of customer data, but know how to get the best out of the data you do have. This can help you to avoid the pitfalls of a lack of data integrity and make sure you are confident in the story your data is telling you.
Take attribution modelling — every brand is doing some level of attribution modelling, but there is bias in nearly every attribution story. For example, if a friend recommends a Westin hotel in city centre San Francisco for my upcoming trip, my first inclination would be to Google that hotel. I would read some reviews on various travel aggregators and blogs and then, ultimately, go to the Westin website and book a room. An attribution model would likely overweigh the referral from Google to the Westin website and not adequately account for all the review websites I visited before. And it’s nearly impossible to capture the impact of the word-of-mouth recommendation. You need someone who can think critically and recognise the nuance inherent in customer data to help you to make the best decisions and get the most out of your marketing budget.
3. A business-orientated thinker.
In recent years, there’s been an uptick of MBA programmes that offer courses and even emphases in analytics (over one third of the top 100 business schools offer a master’s degree in business analytics). Clearly, they see the importance of business leaders who can think like analysts. I think it’s just as important for the inverse to be true — you want your analysts to think like business leaders.
You need people who don’t just see trends in your data, but who understand the implications those trends have on your business and how to communicate them effectively. You need big-picture thinkers. And remember that the impetus doesn’t fall solely on the shoulders of your analysts. You have a responsibility to make sure your analysts have a view beyond their cubicle walls. Those who do become more than just number crunchers. They can become trusted advisors to your business leaders by understanding the implications data has on strategic decisions.
Aligning your teams, your processes and your technology can help you to position your organisation to receive the maximum benefit from your data. You’ll be able to improve your decision making and use your marketing budget more efficiently and effectively. But customer intelligence isn’t just about saving time or dollars and cents. It’s about understanding what makes your customers tick. When you really know your customers, you can start to create experiences that they will connect with in a personal and meaningful way.
Ethan Baron, “Business Analytics Master’s At 100 Top B-Schools,” Poets & Quants, 18 January 2016, https://poetsandquants.com/2016/01/18/business-analytics-masters-at-the-top-100-b-schools/.
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