In my last post, I talked about folding community into your larger business operations to better serve customers and your company, a technique known as Community as an Integrated Service (CIS). I also laid out the fundamentals of PIE (Processes, Integrations and Ecosystems), a framework for building out CIS. Well, every pie needs filling, whether it is blueberry or raspberry. CIS’ PIE filling is Data.
These days, everyone talks a Big Data game, but if you ask me, big data isn't new, it’s just bigger. There’s more of it!
That’s great, because data is key to providing a great customer experience. When data is housed separately in multiple systems, however, it only provides a fragmented view of customers. That’s why more and more companies want to achieve a unified customer database that consolidates profile, engagement, transaction and other information and ensures that it is accessible by all systems.
Community data, which shows how customers are interacting with your brand and often indicates which products and services they may be interested in in the future, should be a key input into this platform. Unfortunately, this is not always the case.
Why is this? First, cross-functional collaboration is hard. Second, we tend to think in terms of immediate transactions vs. building relationships over time (which community enables). Finally, knowledge is power, and different groups within a company don’t always play nice and share their information with others. People are so focused on ‘their world’—the work they touch on a daily basis—that they work with blinders on and, intentionally or not, only focus on their own area.
As a result, very few companies include their customers’ engagement behavior in their lead generation, their support cases or their composite behavior scores. In fact, community data often doesn’t even show up on a company’s main dashboard. So there’s certainly an opportunity for companies to leverage all their data to create a single unified view of the customer.
To help you integrate community data into your larger business PIE (Processes, Integrations and Ecosystems). Below are examples of some best practices, each one touching a different part of the PIE.
Business processes are repeated actions that help companies operate efficiently. Here are three examples of how community data can improve processes.
Instead of setting business goals based on a specific target number, consider looking at data ranges that take the entire situation into account.
Be like a pediatrician, who interprets growth charts in the context of the child's overall well-being, environment, and genetic background. Community KPIs, for instance, need to be looked at side-by-side with other company touch points. And they need to be taken in context—the context of your industry, the size and sophistication of your customer base (e.g. Is your technology so new there’s a long sales cycle?), the context of all other communication channels, and how the data impacts your other channel’s information, etc.
What’s the point of reporting an arbitrary reach number? How can people possibly assess whether any given reach number makes a buy good or bad, once they realize that the value of an impression varies dramatically by channel? Integrate all your data—community data included—into one location so you have full context when setting goals.
Companies need to bring an individual’s and a company’s community engagement information into their central databases. (Notice I didn’t say “data warehouses,”, which can require large investments in infrastructure and development resources, something smaller and mid-size companies might not be able to do.) They should integrate the engagement data into their main customer relationship management systems and make it a key input into their marketing automation systems.
Make the case to executives by pointing out that communities often touch customers more than any other platform or service, except the product itself. That can give your marketing, sales, and customer support teams valuable context into how customers behave and what type of communication would be most useful to them.
Model customer behavior and leverage recommendation engines to push relevant content and information to your community customers. Companies such as Higher Logic enable you to set up triggered responses (based on profile information) to send a follow-up email to a customer (or prospect). You can also forward relevant information to an account manager or sales rep, who then contacts the customer to upsell or cross-sell. Some platforms also have a lightweight recommendation engine based on content tags, keywords, etc.
Unfortunately, only rarely does this information get used to develop behavior models. At Marketo, we scored and modeled our customers’ and partners’ behavior (Did they read an article? Did they start a discussion about a feature? Did they watch a video? etc.). We then served up relevant content (leveraging tools such as Optimizely and Marketo), pushed notifications to their product profile pages, and sent messages to their email inboxes. Those messages were more valuable to customers because we selected them using a full set of data, including information from our community.
Every system in your tech stack should work together, sharing data and helping inform your next move. Without data from your online community, your integrations are incomplete.
Yes, you should aim to have a single company-wide dashboard which includes customer and prospect community data. That’s a tall order, but remember, Rome wasn't built in day, so start simple.
At Marketo, we integrated 12+ different data sources in our dashboard. But we didn’t do it all at once. First we defined and mapped out our data sources. Then we added two new data sources to our centralized database every quarter because taking a simpler, more methodical approach increases efficiency and eliminates duplication.
As Scott Brinker points out, even though marketing technology stacks are relatively “shallow: i.e., passing around fairly lightweight data, such as contact record fields, activity events, and common campaign identifiers — there is nonetheless a basic level of orchestration emerging.” Community can be the hub for all customer and partner (and even employee) interactions.
To integrate your community platform, your customer support systems, and your product information, you’ll need to develop a universal ID. One that stays consistent in all your data related systems (How many of you are doing this today?). Sometimes this ID can be an email address, although using that has its own challenges because people often have multiple email addresses, such as when they change companies, each one having its own email domain. This often causes duplicate accounts and data integrity issues.
At Intuit, I had firstname.lastname@example.org. At Marketo, I was email@example.com. As a result, I have multiple records in Salesforce, a CRM system used by both companies. Consultants and independent workers (who make up 30% of the workforce) often have with multiple clients each with its own email domain. As a consultant, I have multiple accounts (and get treated as multiple people) on collaboration platforms. Others just use different email addresses for different things.
By stitching systems together, you can improve data quality by consolidating accounts. That gives you a better view of customers so you’re making decisions based on the right data, not skewed information from one or two isolated systems.
Whether it is community data or just plain old marketing data, there’s lots of opportunity to sprinkle third-party information into your database to develop a more in-depth profile of your customers.
We used Datanyze to gather firmographic data, which we used to better understand a company’s in-house technologies and other buying signals. It can also be helpful to understand your current customers and then identify prospects with whom they share certain characteristics. Another example is the use of Inside View to identify ideal prospects from around the web, inviting them to your new community and nurturing them toward making a purchase.
Every business has ecosystems made up of department and staff teams, partners, and customers. Your data can help each one run more smoothly and make better strategic decisions.
Most companies report on community analytics to the rest of the organization at the macro level: percentage of customers using the community, percentage of posts with answers, some aggregate engagement metric, etc. Few leaders dig into the data and ensure that various parts of the organization are integrating this data into their everyday business processes. For example, I have yet to see a customer success team request this information.
Community leaders need to do internal road-shows and educate their co-workers about the rich, detailed data online communities provide and how that data can help other departments. Your customer success team may be much more successful at working with frustrated customers by spotting problems early, when customers first post a message in your community versus when they finally call for help. At Intuit, we often focused on key correlations that others in the organization would understand. For example, we often compared call-driver data to trending issues on community.
There are way too many disjointed efforts when it comes to using data as a strategic weapon. Companies let internal politics, office layouts (e.g. Why doesn’t marketing sit with the product team?), and management’s hesitancy to democratize data and put the proper tools and information in the hands of every employee.
Delivering an exceptional customer experience means that employees must develop a unified view of the customer journey. How can they do that if they don’t have access to the same data? In other words, customer engagement data needs to be captured across channels and websites, and shared across the company.
At Marketo, one of our sales reps asked if I could provide her with the number of employees from her account (company) that had logged into community. My team provided her something even better: A list of all customers, their community engagement scores, each person’s frequency accessing the community and a list of all content they viewed or interacted with. This information was forwarded to her Salesforce instance so she could follow up directly in a timely manner with her key contacts within that company.
Because she had all the relevant information, our sales rep was able to provide more relevant and personalized communication with account stakeholders. If your company takes this same approach, you’ll be one step closer to building positive, long-term relationships with your customers.
The above are just some aspects of the PIE. If companies want to excel, they first must experience a digital and data transformation. Capturing, understanding and leveraging community data is first step in making this happen.
Imagine analyzing real-time engagement information and pushing personalized messages to your most outspoken advocates or critics, your business partners, prospects, customers who may be at risk, and those who are eager to deepen their relationship with your company. Your customers would be more engaged and satisfied, and your business would reap the benefits.
This community data, however, should not be separated from your other systems. It needs to feed into your marketing stack, support systems and even your products (and of course into your data warehouses, if you have time and resources to build one).
According to Aberdeen Research, 85% of companies struggle in this area because their data is captured and stored in disparate, disconnected systems. I like a good challenge, however, and am helping different companies connect the data dots and produce a single customer profile.
Maybe integrating all customer-facing data is too much to ask. That’s one reason I haven’t even discussed Artificial intelligence or machine learning, which is beginning to impact all data systems because of the convergence of data, computing power, and algorithms. But AI is coming. For community managers, it will enable you to give community members, creators, collaborators and lurkers, personalized recommendations, intelligent search results, and automated information.