So how can marketers deliver the friction-free options customers expect? After all, they don’t know or care that solutions were purchased based on a previous model of display-based marketing, or that there are separate, siloed departments for everything from media to customer experience. Thanks to the new always-on, real-time world, they want increased personalization; relevant and timely messaging; and cross-channel targeting even when they are constantly on the move, engaging with brands across multiple devices.
So far, marketers have not been able to support these consumer desires to the extent that consumers expect — not only in terms of interacting with brands directly but what companies can do based on customer information they already have. For example, imagine an airline that serves up an inappropriate ad that clearly doesn’t fit in with what the customer has indicated she is interested in. Perhaps that customer has a yearly WiFi membership with the airline, but when she opens up the airline’s mobile app, a message always pops up asking if she wants to buy internet access — even though she already has it and the airline must have a record of the WiFi subscription.
Since she is already very engaged in what the airline is offering her, to see something so off-base is somewhat insulting. If a company hasn’t used the information she has provided to make her customer experience more personal and relevant, that’s bad for business as well as for consumer experience.
The solution to the above problem is both the biggest opportunity as well as the toughest challenge marketers face right now: to truly understand the customer across channels. As digital marketing has evolved from just a few channels to an explosion of devices and touchpoints, each with its own siloed team, technology tools and customer data sets, connecting data and resolving identity may seem like a tall order.
A Signal study found that companies use on average of 12.7 different tools daily to go about their marketing business. Another 9 percent use a whopping 31 different tools. It’s difficult to even maintain that many systems, let alone get value out of them. This high level of complexity is completely invisible to the consumer — if they knew how many different systems are involved in generating an ad impression or marketing to them, they might think marketers are crazy!
Within those systems lies the challenge: fragmented customer identity. Above was an airline example. Of course, airlines have the benefit of connecting frequent flyer numbers to their customers, which, in theory, should make tying all of a consumer’s different identities together across channels a realistic possibility. But even in that case, execution is still difficult.
So, a company that doesn’t have the luxury of any unique customer identifier across channels has an even more important task to try and come up with a solution that can center around the customer.
The reality is that marketers need help — from across people, process and technology standpoints. They need new tools and skill sets that involve foundational projects which will require a significant time investment and reorganization. The first impulse tends to be to chase the next shiny object— the latest advertising solution or trendy social media tool.
Instead, marketers need to transition from a campaign one-off focus to treating data as a discipline and a priority. There are clear things marketers can do to make sure they progress along the data maturity model and tie marketing results to concrete business objectives and outcomes.
With the right actionable plan focusing on people, process and technology, with wise and well-thought-out investments, marketers can start making significant progress towards catching up with their ever-evolving customers in just one year. These are Signal’s five key recommendations for marketers to set themselves up for success. Some of these steps may occur concurrently, or evolve one into the other. However, all of them are necessary elements to bring to bear in order to reach today’s consumers in the ways they expect.
When it comes to data, marketers must look at where they are to figure out where they’re going. The foundation of any data-driven effort is a current-state analysis, to understand what the data and technology landscape looks like today. Unfortunately, many companies don’t have a full picture of what customer-level data they already have, partly due to departmental silos. Perhaps someone in customer acquisition may focus on different data points than someone in charge of retention or customer experience. And if the company works with an agency, they may not be privy to what specific data points from third-party providers the agency uses. So taking stock of the entire customer data landscape is essential.
After all, marketing stacks tend to evolve over time, so typically a lot of overlap starts to pile up among different systems and solutions. This exercise provides the opportunity to streamline and optimize the marketing technology stack. As a result, companies can begin to get some revenue impact out of cutting things they no longer need and make sure they really focus on the data points that do matter for the business.
For example, marketers can look at how they are using first-party, second-party and third-party data and examine where they need to augment their current view of the customer and where they have overlapping information.
Are they focusing on third-party data points solely in the programmatic setting? If so, they’ll need to transition into a more holistic customer experience, of which media channels are just one part.
Examining the current data state is an opportunity to get ready to transition out of a siloed ecosystem, starting with knowing what the company has today.
In order to take stock of the current data landscape, marketers need to start with a comprehensive data audit — that is, catalog and classify all relevant data points, their source systems, and how they are being used.
The evolving nature of the data space means multiple initiatives have likely been conducted across different marketing teams; and incremental investments in acquiring and collecting customer-level data have been made. A data audit helps companies effectively leverage all the data that they currently have available.
Even something simple in data ontology, such as how an impression, lead or conversion is defined, can make a big difference in determining the truth in how data is used. If a company is buying media from a variety of sources, for instance, and all of them call an impression something different, then when the data is integrated it won’t match up. A robust data audit is important to build a single table that shows data points.
As marketers take stock of their current marketing data landscape and conduct their data audit, they need to break down their strategic objectives as specifically as possible. For example, instead of generally saying a company wants to be the best cross-channel marketer in a vertical, the overarching goal should be broken down into steps. The first step, for instance, could be to convert one-time shoppers to repeat shoppers. Another really useful opportunity might be to look at customer segmentation and identify ways to move and shift customers from lower value to higher value segments. For instance, if there are different levels in a frequent flyer program, moving customers from the lowest to the second-lowest tier can offer a big increase to the bottom line. This is a great way to start creating value in a short time frame instead of shooting for the moon.
The audit process should be approached by first identifying all relevant data stakeholders. While the exact lineup tends to vary from organization to organization, a company will typically need functional leads from various channels of marketing (e.g. display, mobile, etc), CRM, customer experience, customer success, and, in many organizations, IT.
Then, half-day workshops can be held with core stakeholders during which each identifies data points and source systems they are currently responsible for, either directly or via partnerships with various vendors (such as third-party data providers). Document and share back details about each data point (see figure 1) with all stakeholders, so an entire customer picture can begin to emerge.
After the audit is completed, an excellent baseline will exist for data-driven marketing efforts. More importantly, the company will have identified immediate opportunities that affect revenue, such as the ability to reduce spend on third-party data, or targeted personalization to high-value customers that reduces time between subsequent purchases.
Map out what’s missing to identify what is still needed. The data audit will identify what customer data points marketers currently have access to, which means they can begin mapping out which data points they don’t have today but need in order to meet marketing objectives. For example, a company might be launching a new product targeted to new parents, but don’t have a way of discerning someone’s life stage from existing data assets.
The quickest solution is to acquire this data point through a third-party data vendor — which requires evaluating potential third-party data partners and selecting the best fit. Marketers should be proactive – rather than settling for a stock segment (e.g. ‘new parents’), they should be prepared to ask questions on data origin, freshness, and quality (for example, is this inferred data or sourced from verifiable public records, how often is it refreshed, what are the rules for qualifying in this segment/with this attribute, etc). A longer-term solution will likely involve designing partnerships to ensure access to high-quality, second-party data as well as enhancing the use of first-party data (if applicable).
Once marketers have a sense of where they are now in terms of their data landscape, they can begin to build a roadmap of what they need to execute to achieve their strategic goals.
This is an actual document that includes several deliverables and will serve as a blueprint for the organization’s implementation of data-driven initiatives, as well as an action plan that diverse, global teams can rally around to execute. These deliverables include a “desired state” and how it maps to strategic objectives; a gap analysis report; a detailed roadmap of initiatives needed to reach the desired state, including people, process and technology considerations (in three flavors — conservative, realistic and optimistic); an ROI analysis; and possibly a formal presentation of this roadmap to the stakeholder team.
Typically, companies fall into two camps as they try to solve highly-complex data-driven marketing challenges. One way is to attempt to “boil the ocean” by bringing in every possible internal stakeholder and purchasing all-encompassing enterprise software. This effort can take years to complete and isn’t in line with today’s speed of innovation.
The opposite strategy focuses on point solutions and hones in deeply on very specific problems. For example, a company might be structured so that a department is incentivized to address narrow attribution issues such as how much display media is affecting sales — instead of taking a higher-level view that looks at data across multiple teams to learn about how customers shop.
The best strategy is to find a happy medium between the two above scenarios. A detailed data roadmap will help determine a tangible set of accomplishments that can be done in a set, reasonable amount of time.
Creating a data roadmap and organizational data foundation is best tackled via incremental initiatives that generate ROI or hit key markers along the way. So, marketers should make sure to identify their own tangible objectives based on the marketing organization’s short, medium and long-term goals.
For example, many marketing organizations set out to improve areas such as personalization or customer experience. Breaking that down to what’s achievable in the next three or six months, and then beyond that timeframe, will surface more tangible objectives. For example, converting one-time purchasers into repeat shoppers can be something to be tackled in the next six months. That project then leads to a more comprehensive initiative to move customers from a lower-value loyalty segment to a higher one, through a mix of media and on-site personalization.
Instead of “boiling the ocean” or hyper-focusing on one granular problem, companies should understand and identify key marketing initiatives that they determine will offer the most ROI in the shortest amount of time.
Remember, it isn’t possible to transition from the current state to cross-channel marketing success through one massive technological leap. Instead, it takes smaller, incremental steps as a variety of technological options are tested. Then, the company can settle on what works best and innovate from there.
A gap analysis will identify granular steps the organization needs to make in order to meet its strategic objectives when it comes to data and its supporting technologies to move forward from a current state. To extend the example from the previous section, perhaps a solid first step might be to tailor messages to loyalty program member emails according to their level of spending or lifetime value. If there are increases in lifetime value after a fairly short test period (e.g. 3 months), a subsequent step to continue building on the ROI of this initiative could be to partner with a retail chain to help better understand a particular high-value segment’s spending habits.
Integrating data from multiple touchpoints into a centralized data foundation that supports cross-channel marketing is no easy task.
That centralized data foundation includes a high level of responsibility — it must be accurate and used to enhance the overall customer experience which from a customer’s perspective increasingly also extends to media. Most companies still struggle to integrate new technologies and platforms so they can begin to get a unified view of the customer and move away from separate, siloed engagements — where mobile app interactions are kept entirely separate from web data, for example.
Marketers know that data centralization is well-worth it — the foundation itself can bring tremendous value and efficiency to their integration needs. But to move towards that goal, they need a sense of organizational alignment among multiple stakeholders around a clear data strategy.
Identifying the data stakeholders in an organization, within marketing and outside of it, is essential in order to align it around a clear data strategy. So, who owns customer data at the executive level? This usually starts within marketing or a CRM department, so it might fall under the CMO or COO. But, it could also be outside of marketing entirely — in traditional enterprises, it might fall under IT.
Once a clear owner of customer data has been identified pinpointing the stakeholders in that sphere of influence is an easier challenge. If it’s unclear who owns customer data in the organization, a stakeholder exercise becomes important: for instance, if several systems collect the same data sets, with slight variations, but are owned by different teams, the company will have to determine who is responsible for what within the context of a larger corporate data strategy. Luckily, all the relevant stakeholders and influencers may have already been identified during the process of conducting a data audit.
At this point, customer data stakeholders will need to be aligned around the goal of a data foundation. Really important wins can be attained just by looking at the organizational structure today and identifying opportunities to make sure customer level data is being shared correctly — whether through technologies or reorganization, so there is a clear importance placed on the goal of a universal customer view.
The most complex challenge of getting this done right is organizing the business around the consumer, not the channel. Consumers are so rapidly adopting additional devices and channels that marketers can’t keep up. As channels pile up, it’s clear that businesses are getting overwhelmed and additional P&Ls are becoming unrealistic. Channel planning will be forced to morph into a consumer-centric view of the world.
Some companies are already doing this very well. For example, Disney does a great job of organizing around the customer. The Magic Band experience is a clear example of how marketers need to align every part of organization to be able to execute this kind of vision.
Marketers place a great deal of value on marketing technology. But in order to implement successful data-driven marketing across devices and channels, the people and process parts must be addressed as well. After all, technology is just an enabler of people and processes.
A good analogy is to consider who owns the customer in the organization. Is there one executive who is tasked with “owning” the customer, such as a Chief Experience Officer?
For many organizations, there is no single owner who thinks in these terms. This is where people and process come heavily into play in today’s modern marketing universe. It’s a fundamentally different way of thinking about marketing: to have a business focused on the consumer rather than on channels, and to consider how to use each channel not only to broadcast marketing messages, but to reach consumers on their customer journey.
People, process and technology has to be a balance, too: far too often, business transformation efforts concentrate on process improvement strategies and business process reengineering, while essentially ignoring the people aspect. Subsequently, these transformation initiatives do not achieve their desired results.
Customer data points are typically gathered from multiple channels and systems, across an entire organization. It is important to define the framework for correct data collection, labeling, storage, access, and controlled sharing of all customer-level engagement information that is relevant for marketing and customer experience.
First, responsibilities and levels of access need to be defined and enforced (for instance, perhaps a marketing manager in a country can change business rules for data collection for that country only). Next, extensible data taxonomy and customer data classification systems are necessary, underscoring what types of usage each relevant data category can have, as well as an identification of the “golden record” — the highest-quality version of each data point. Finally, defining and adhering to the data life cycle rules (for example, perhaps transactions older than 12 months are deemed no longer relevant) is a must.
With today’s rapid marketing evolution, it’s important to continuously evaluate technology from a data perspective and understand where there are gaps — delving into key channels first.
For example, with consumers shifting more of their content consumption to mobile devices, is the company able to track the same engagement in loyalty emails as they would if users read them on their desktops?
A recent Signal study revealed  that a majority of marketers use more than 10 different technological solutions in their dayto-day operations. In essence, marketers have inadvertently become system integrators in an increasingly complex marketing ecosystem. It’s time to think about how this technology stack was developed and determine how technology decisions can be made that help the organization become more efficient, consumer-focused and optimized for cross-channel success.
Some immediate ROI may be attained by looking at optimizing how data is passed between all the different systems used in day-to-day marketing. For example, is the same customer-level information collected multiple times, perhaps via a web analytics vendor and a downstream activation partner like a retargeter or DSP? By centralizing data collection into one system, marketers can achieve greater control over their customer data; minimize data leakage; and ensure they are adhering to any data governance policies they have established.
Given the pace of innovation and changes in available technology, another good option is for the company to map out what the entire technology stack would look like if it had the opportunity to build it from scratch today, as opposed to the evolutionary way most stacks are built. Decisions were likely made about a mobile marketing provider long ago, before a data unification layer and panoramic customer view were so essential. If business objectives have changed from a data perspective, marketers should decide whether they are getting all the necessary user-level data from all the components of their stack. A different approach to data collection on mobile may be needed, for example, because of the increasing consumer shift to this particular channel.
It’s easy to be swayed by industry “noise” around technology, where every year is the “year” of something. The reality is, not everybody needs every piece of technology. It’s important to understand what the company’s expected ROI is and challenge its vendors to demonstrate what they think marketing ROI will be with their technology — because during the sales process it can be easy to walk away with unrealistic expectations.
Marketing team members are essential to support a move towards a centralized data view, since they will certainly be the ones to reap the immediate benefits of a rich customer-level data store, including improved campaign activation, personalization and better messaging to high-value customers.
So, marketers should examine what key KPIs people on their teams are tracking. Because data was initially tied to media activation, companies still tend to look at campaign-driven elements, such as using data to reduce effective CPM across the board, or using data to increase effective reach within the constraints of a campaign. However, it’s important to start moving beyond how data is improving a specific campaign investment.
A real KPI has nothing to do with a click — it has to do with determining what event or activities predict what marketers want a customer to do, whether buying a car or submitting a lead online. The overall trend is moving towards a marketing world that can predict what actions can be stimulated that will make a prospect or customer engage or make a purchase. Data will be the fuel for this predictive world.
In order to transition into a data-driven organization, marketers can’t remain stuck in a piecemeal, campaign-oriented mindset. It needs to be a high-level execution that is an organizational priority. No matter who owns the systems that touch customer data in the company, customer-level data needs to be treated as a valuable corporate asset that is integral to achieving strategic objectives across the organization.
If brands begin to think about data as a corporate asset, a different set of KPIs start to emerge. How deeply is the customer known? How complete are customer profiles today? What percentage of all interactions with customers are being captured in a profile?
Digital marketing ecosystems are typically complex and constantly evolving. But, teams responsible for key channels can be incentivized through their new KPIs to collaborate on data-driven initiatives — so that the search team is aligned with the display team, for instance. Or if there are multiple brands, is each brand’s marketing treated entirely separately? Could quick wins be attained by combining what each brand knows about the customer?
To achieve cross-channel success, it’s not just about technology. It’s not just about people and process. It is a mix of these three things, functioning well together, that will help marketers move forward.
Those companies that figure out how to balance and execute across these three areas are going to be successful, while those that focus on just one, or two out of three, will have a harder time and require more time and effort to progress along that data maturity curve.
A customer’s fully-fleshed-out, cross-channel identity — discovered by leveraging first-party data and merging activity across multiple channels and devices — has become a valuable asset that can be used to improve measurement, attribution, personalization, messaging and ultimately a customer’s overall experience. However, this means overcoming the daunting challenge of integrating data from disparate touchpoints — including web, mobile applications, email, media impressions, in-store, CRM and other relevant marketing channels. In addition, data management is complex and ongoing, so it is not a simple, short-term win. It requires a longer-term view and path to ROI, which many campaign-focused marketing departments are not accustomed to implementing.
Resolving a customer’s cross-channel identity, with the goal of a unified customer view, is a complex challenge that many marketing organizations have been slow to take on.
The cross-channel identity opportunities available in today’s marketplace makes these complex initiatives well-worth the effort. And companies can make significant headway in just 12 months, or even less, by clearly identifying tangible, iterative milestones along the way to a fully data-driven marketing environment. By tackling issues related to people, process and technology across the organization with the goal of supporting a centralized data foundation, marketers can move beyond short-term campaigns to ensure long-term success.
The pace of innovation has sped up. Progressive marketers will see the single customer view as the future-proof foundation that is necessary for today’s modern marketing — that can accommodate current needs yet also prepare them for the next technological disruption down the line. After all, who can reliably guess what will occupy the place of the smartphone eight years from now?
The digital marketing environment is complicated and constantly changing. To be successful in this landscape, organizations need a solid, strategic plan for driving more ROI from their data and technology investments — that means looking at customer engagement data in a whole new light. The forward-thinking CMOs that take on this challenge will be way ahead of their competitors in terms of providing customers with a seamless path-to-purchase. However, the single customer view is not an end in and of itself – it ensures that marketing organizations are nimble and can match changing consumer expectations.