Digital consumer intelligence (DCI) enables organizations to adapt to a fast-changing world by connecting decision makers to strategic insights derived from a combination of real-time online data, customer data, and marketing intelligence.
Brandwatch introduced the term in 2019 to describe the evolving and expanding ways in which its customers were using its data and technology. It’s a response to a number of societal and technological shifts, from rapidly evolving consumer needs to the proliferation of great AI and technology that businesses (both established and emerging) are using to get a competitive advantage.
Across 13 years in the field, Brandwatch has observed a number of behaviors that typify a healthy approach to incorporating consumer intelligence into decision making. These behaviors develop over time from standard use cases that are based on monitoring through to incorporating consumer intelligence into almost every major business decision.
For example, Nestlé’s Marcel Heitmeier recently shared the story of the organization’s development in digital consumer intelligence. Previously, social listening projects were conducted by agencies and insights developed within these studies were not shared beyond the team that owned them. More recently, Heitmeier was involved in developing a centre of excellence for social intelligence which drastically improved the flow of information by sharing insights with and informing action across various business functions. Not only did this result in significant cost-savings, but the organization took ownership of the generation of actionable insights and developed a culture of data that’s brought teams closer to the needs of consumers. Now, social data (data from public social media conversations) is being combined with all kinds of other sources and it’s an integral part of market research at Nestlé. It’s informing teams you’d traditionally associate with social data, like marketing functions and corporate communications, but also being incorporated into product development, HR, and many other areas of the business.
To help organizations understand their maturity in digital consumer intelligence, get the most out of their data, and make better decisions, we’re introducing the DCI Maturity Model. It’s designed to provide a framework from which organizations can structure and plan their maturity journey, showing how they can level up across a number of industry-defined pillars.
“We are now at a point where businesses are really taking social data and digital data very seriously. But up until now there’s been no way to measure how your organization is doing. The DCI Maturity Model is a framework that enables deeper, more meaningful conversations internally as well as with other professionals in this space, helping organizations to grow in maturity and learn from each other.”
The four stages of Digital Consumer Intelligence Maturity
For each stage, there are a number of expected behaviors or use cases that can help you understand where your organization fits on the model. It should be said that there is no necessary connection between the size of an organization and its DCI Maturity. In fact, slower moving conglomerates might find that there is a lot of work to be done to mature into DCI and better understand their consumers.
1. Monitoring
The earliest stage of DCI Maturity is characterized by the following behaviors:
- The monitoring of one or two data sources. Eg social listening or Net Promoter Score data.
- There is likely a single non-analyst gathering and reviewing the data.
- Data informs a small number of use cases, and impact is minimal beyond one team.
- You could describe the monitoring stage as ‘Counting numbers for the sake of numbers’.
Common use cases associated with the monitoring stage are:
- Collecting overall metrics to provide regular reporting over time, and detecting major patterns or shifts.
- Eg: Marcoms teams measuring website traffic, online share of voice, or social media engagement
- Eg: Customer Services measuring support tickets and customer satisfaction scores.
- Little to no segmentation, text analysis, or cross-referencing with other data sets.
Example:
You can find some examples of the behaviors linked to this early stage of digital consumer intelligence maturity in our case study with Manchester Central. Samantha Pike-Devlin, Digital Communications Executive at Manchester Central, uses Brandwatch for a number of different use cases including share of voice analysis and campaign management. Monthly reporting helps the team to stay on top of their online presence as well as to inform the business on the impact and outcomes of their activities. Following excellent feedback, Pike-Devlin is keen to expand the role insights play beyond her own team, moving from reporting to actively informing activities like improving customer experience, and in doing so leveling up the organization’s maturity in DCI.
2. Developing intelligence
The second stage of DCI maturity can be recognized in the following behaviors:
- Best practice analysis of various data sources. For example, analysts will often attend data analytics training sessions with vendors or industry experts and bring that knowledge to their work.
- Colleagues proactively ask questions of the data, and it’s continuously informing team activity.
- Data is used both in reporting and live insights.
- There is limited impact beyond one department. For example, there may be some mainly ad-hoc analysis for internal teams and stakeholders.
Common use cases associated with developing intelligence:
- Continuous consumer and market analysis.
- Ongoing segmentation and data collection, with some ad-hoc analysis provided to other internal departments and stakeholders on a project basis.
- This may include analysis of more in-depth customer cohorts or target personas over long-term, program-orientated initiatives.
- Insights are generated from the combination of multiple data sources, drawing connections to revenue and other company objectives.
Example
Mahaka Radio Integra oversees seven major radio stations in Indonesia. Putra Priyandara is a Brandwatch power-user, responsible for data analytics and producing key insights on consumer behavior that drive revenue. Priyandara’s team serves as a command centre, with high demand from across the organization. Their insights, which are drawn from a range of data sources, help with all kinds of activities, including informing pitches to advertisers, improving radio station content, and managing and creating engaging content for social media. These insights helped them achieve a threefold increase in engagement from prospective ad buyers during the pandemic when they were able to prove that people were still listening to the radio despite the overnight disappearance of the regular commute during lockdown. You can read more about how Priyandara’s team works in this case study.
3. Digital consumer intelligence
Organizations in the third stage of DCI Maturity tend to operate in the following ways:
- Blending and triangulation between multiple sources of consumer insight.
- Eg: Advanced social intelligence together with first and third party data are brought together from their specialist platforms into purpose-built data manipulation environments.
- Insights inform business decisions across a whole span of organizational functions.
- Data and insights are accessible beyond the team that ‘own’ them.
- Eg: Insights from support survey responses are available to the operations and sales teams, while social data might inform both marcomms and product development.
Common use cases associated with digital consumer intelligence include:
- Informing business decisions across a whole span of organizational functions and internal stakeholders.
- Analysts will partake in everything from historical trend analysis to forecasting and predictive analytics.
Example:
Marcel Heitmeier at Nestlé and his team have developed a Morning Briefing Dashboard to help stakeholders from across the business understand what’s happening in fast-moving consumer conversations. The dashboard contains a highly curated set of insights segmented by panels, topics, behaviors, and marketing performance. All these aspects are connected to a custom email alerting system which keeps key stakeholders informed throughout the day. This dashboard, alongside all the other work Marcel’s team have done in developing a centre of excellence in social intelligence and starting to blend social data with other sources of consumer insight, is a great example of digital consumer intelligence at work.
4. Embedded digital consumer intelligence
Organizations that are operating at the highest level of DCI Maturity will exhibit the following behaviors:
- Almost every major company decision includes input from DCI.
- There are significant human and financial investments in this area, and an organization-wide recognition of the competitive advantage DCI brings.
At this level, inputs from DCI inform major company investments. These inputs will also be the basis for the reporting that is actively consumed by the C-suite and decision makers across the organization.
Example:
Brandwatch’s most successful clients are those with an organization-wide understanding of the value of consumer insights. They’ll often make use of tools like Brandwatch’s Data Upload API, which allows customers to upload their first party customer data alongside online data and analyze it with all of Brandwatch’s leading AI text analysis capabilities. The idea is to build a holistic view of their voice of the customer and connect consumer insights to hard ROI metrics to inform everything from product development to quality control, as well as major organizational pivots.
How to approach your DCI Maturity strategy
It might be easy for you to spot where your organization fits within the four stages. The hard part is formulating a plan and making changes that mean you can move to the next level and towards the goal of embedded digital consumer intelligence.
Your approach to maturing can be made easier by:
- Starting small but with purpose: If you’re still just setting up tracking, make sure you’re collecting the metadata you’ll need to be able to segment and analyze in meaningful ways later on.
- Investing in trustworthy tools: The world of data can be like the Wild West. You’ll need tools you can rely in terms of the customer support, compliance, and experience they offer.
- Not fretting about perfection: Your data strategy will be refined countless times over the years. The key thing is to start telling compelling stories with data. This is what drives actions – tangible, memorable insights that the whole company refers to and uses to hold on to a common purpose.
It’s also important to remember that an organization can move both up and down the scale of maturity. Complacency or slow-moving progress could mean that an organization that is relatively mature today could fall to a less mature stage as time goes on.
Next, we’ll share the six key areas you can focus on to help your organization become more DCI mature.
The six pillars of digital consumer intelligence
To mature in digital consumer intelligence, an organization must focus its efforts on six key areas. These areas are what we call the six pillars of DCI.
“The pillars are really good guiding principles for where we need to be heading. They provide a blueprint for mapping where you are in the maturity journey and where improvements need to be made in the short and long term.”
Here they are in more detail.
Governance: Collect and manage your consumer data effectively
Typically, an organization’s approach to governance can be mapped against the DCI Maturity Model in the following ways:
| Monitoring | Developing intelligence | Digital consumer intelligence | Embedded digital consumer intelligence |
|---|---|---|---|
| There is limited awareness of data compliance needs. There may be multiple, inconsistent data sources and workflows. | Data compliance requirements are being met. Some data auditing is in place, and efforts are underway to improve efficiency. | There’s a ‘single source of truth’ data policy. The organization goes beyond data compliance standards to anticipate future needs. | There is an effective process to handle nuances between multiple data sources. The organization exceeds compliance standards, taking a proactive stance, ie: data ethics and sustainability. |
Read our full guide to data governance.
Insights flow: Get the right info to the right people across the organization
Typically, an organization’s insights flow can be mapped against the DCI Maturity Model in the following way:
| Monitoring | Developing intelligence | Digital consumer intelligence | Embedded digital consumer intelligence |
|---|---|---|---|
| Access to data is limited to one or two teams. Insight requests are ad hoc, often top-down, without a lot of consultation. There are likely multiple blind spots and silos across the organization that need to be addressed. | Findings are shared with multiple teams on a regular cadence, with some close-to-real-time insight. There is some limited visibility among senior leaders. | Insights are shared high and wide across the organization, often from one central digital centre of excellence. Information is catalogued for future use or reference. | There is broad access across the organization to a growing insights library. There are multiple devolved digital centres of excellence and delivery formats (eg: emails, videos, newsletters, lunch and learn events). |
Read our full guide to insights flow.
Speed to insight: Understand and act on consumer feedback quickly
Typically, an organization’s ability to act on consumer feedback fast can be mapped against the DCI Maturity Model in the following way:
| Monitoring | Developing intelligence | Digital consumer intelligence | Embedded digital consumer intelligence |
|---|---|---|---|
| Consumer insight projects are measured in months and may often be overdue. Significant time is spent on repetitive task stages. | There are some live insights, although these may be limited to simple and preliminary findings. There is some movement towards automation based on needs of prior projects. | Internal resources are optimized to balance insight speed and quality. Analysts are able to pivot between urgent and detailed consumer intelligence projects. | The organization at large is able to predict and resource against future needs, including burst capacity. It’s able to outpace the market. |
Read our full guide on speed to insight.
Culture of data: Create a company-wide mindset of embracing data
Typically, an organization’s ‘culture of data’ can be mapped against the DCI Maturity Model in the following way:
| Monitoring | Developing intelligence | Digital consumer intelligence | Embedded digital consumer intelligence |
|---|---|---|---|
| Decisions can be based solely on instinct and there may be a lack of trust and confidence in data quality. | Consumer insights are routinely presented to leadership during the decision-making process. Those insights are not yet consistently absorbed, understood, or acted upon. | Leadership champions using consumer insights in decision making. Organization’s commitment to consumer intelligence may be visible on their website or discussed in the recruitment process. | Consumer data is visible and meaningful at every level of hierarchy. Consumer insights are prominent within company mission statements. |
Read our full guide on building a culture of data.
Experimentation: Create and test new solutions
Typically, an organization’s approach to experimentation can be mapped against the DCI Maturity Model in the following way:
| Monitoring | Developing intelligence | Digital consumer intelligence | Embedded digital consumer intelligence |
|---|---|---|---|
| There may be resistance to change at this level. Review of current sources, methods, and workflows is limited. | Incremental improvements are made to current sources, methods, and workflows. | There are deliberately designed ‘safe spaces’ for acceptable risk, while new ideas are celebrated and rewarded based on outcomes. | New ideas are celebrated for originality and being tied to performance outcomes. At this level we’ll see novel applications of existing sources and early adoption of new research methods. |
Read our full guide on experimentation.
Voice of the customer (VoC) strategy: Make informed, customer-centric decisions
Typically, an organization’s VoC strategy can be mapped against the DCI Maturity model in the following way.
| Monitoring | Developing intelligence | Digital consumer intelligence | Embedded digital consumer intelligence |
|---|---|---|---|
| There’s a vague or limited connection between consumer intelligence and the company mission statement. | Strategy, once drafted, is verified in light of VoC data. Efforts are underway to tie consumer intelligence to ROI, but this may be heavily dependent on soft metrics and qualitative assessment. | VoC data plays an active role in most strategic projects, while understanding of the consumer moves beyond brand-centrism. | VoC data is integrated as a required, early stage in all strategic planning. Consumer insights are explicitly tied to hard ROI metrics. |
Read our full guide on voice of the customer strategy.
Your next steps
So now you have the framework from which you can build your plan for maturing in digital consumer intelligence. Change is always daunting, especially when it involves a whole organization. But remember the advice shared above on starting small but with purpose, investing in trustworthy tools, and not worrying too much about perfection.
Just one key insight that offers a fresh angle on a pressing challenge can be all it takes to kickstart a culture shift, sparking conversations and an appetite for more.