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First, a summary of the current environment regarding marketing and customer engagement:

• Digitization has changed the way consumers interact with brands;
• A digital world produces data, lots of it;
• The creative aspect of marketing will never go away, but to succeed, now Marketers are also challenged to develop a competency around quantitative analysis;
• Going forward, marketing organizations must become ones that capture, analyze and leverage data;
• Leveraging data represents both a challenge and an opportunity. If marketers can succeed, if they can become data-driven, if they can employ data, process, and analytics to better allocate their resources, they can create a sustainable competitive advantage.

The first step in transforming a Marketing organization into one that is more analytically-focused is to develop five separate but interconnected strategies the consider each of the following dimensions: customer interactions, data, organization, technology, and analytics. Because these dimensions are inter-connected, each of their strategic plans must be consistent and in alignment with each other.

It is becoming increasingly recognized that ensuring a positive customer experience helps achieve measurable success in several important metrics, including revenues; thus, it is critical that companies think carefully about how they interact with their customers. Creating an analytically-focused marketing department starts with this important subject. Leaders in managing the customer experience pursue three imperatives simultaneously:

1. Consider the value proposition for different customer segments when designing the customer experience. A more engaged customer will receive different offers or communications than an occasional user;
2. Deliver value to customer segments by focusing on cross-functional collaboration. For example, Sales might log customer information that could trigger a Marketing action;
3. Develop capabilities to deliver a consistent customer experience and how to keep innovating and improving the experience. Have tools to help with customer-focused planning and executing; know what customer-based metrics need tracking; and offer customer-focused management incentives to keep employees’ goals in line with the company’s goals.

Clearly a customer interaction strategy needs to transcend the enterprise and be constructed from the outside-in (i.e. with the customers’ view as the focus) otherwise companies risk making the same mistakes that were originally made with Customer Relationship Management (CRM) systems. CRM systems were supposed to be customer-focused and help build better customer relationships. However, initial deployments of CRM applications were complicated internal tools, creating silos of customer data and harming the likelihood of enriching the customer experience.

Today’s customers don’t want their relationships managed; they want to be in control.

To develop an effective customer interaction strategy, marketers must first understand and document the entire journey that customers (or prospects) take, from first contact through purchase and finally customer support and retention. This can and should become complicated as there are usually several different “journeys” that a prospect can take. Then marketers can map the necessary changes across organizations, systems, and data to transform and deliver a customer engagement plan that improves the (implied) conversion rates of each of those metrics through those processes.

Taking a data-driven and analytical approach to marketing requires data, obviously. Data is frequently located in different information silos throughout the enterprise; therefore, the development of an enterprise-wide data strategy cannot be understated. Furthermore, the creation of this data strategy must be championed as a partnership between IT, Marketing, and other relevant business operations and tie with the business objectives of the enterprise as a whole — with senior level sponsorship. Teams must also be mindful of data management issues such as data validity and veracity as well as any compliance considerations in regulated industries. And, Marketing, like all departments, needs to be certain is has the organizational talent to execute.

But first, Marketing must develop its own data “sub-strategy” that will synch with the enterprise data strategy. Recall when eCommerce became a disruptor and created silos of data for transaction and browsing information. So did CRM implementations before that. And Enterprise Resource Planning (ERP) implementations before that. Marketing should not duplicate this mistake when considering new technologies. The Marketing data strategy, as well as any associated processes and work streams, must be symbiotic with the enterprise-wide data strategy. If not, then Marketing will create yet another information silo and further fragment the information required to create the 360° view of the customer.

The data strategy for Marketing should center around the data required to support business objectives — the primary business objective being to improve the customer experience — but also include others such as increasing Marketing ROI and other metrics. In most cases Marketing Organizations will already have articulated their business objectives, but this exercise could prove to be enlightening because of the possibilities that enhanced data capabilities will create. Marketing should brainstorm with IT and other technologists to determine the data structures required to improve customer retention, lessen customer acquisition costs, measure the ROI of Marketing’s efforts, and after they get more advanced employ predictive and optimization algorithms.

So, what is the starting point to develop a Marketing data strategy? Begin with a pilot that is well-defined, with a short timeframe and clear, tangible outcomes. Best practices suggest focusing on a manageable portion of a business objective, such as understanding the path of a prospect through their decision journey to help improve customer acquisition. Consider data needs and sources. Results will identify resource needs for broader application and allow executives to see the benefits — and prove the value — of adopting a holistic data strategy.

Making upgrades to data and analytics infrastructure requires input from all stakeholders of corporate infrastructure, from Sales and Customer Support, to the Supply Chain and Operations to Finance and Accounting. Data and analytics cross organizational barriers and will challenge conventional approaches, so expect analytics to disrupt some traditional organizational structures and silos. As a result, members of the executive suite need to work together to revisit organizational models, evaluate current structures, and design approaches to maximize revenue growth in this new world. Most companies are only beginning to understand the organizational design opportunities presented by analytical marketing.

To better evaluate their organizational strategy, companies should consider building cross-functional teams, assessing their analytical skills, creating shared goals, aligning existing goals and compensation, and launching a pilot to demonstrate the benefits and results the team (and company) can gain from sharing their data and collaborating.

Analytical marketing and pursuing a data-driven approach require new types of expertise across your organization, and that alone can be incentive enough for restructuring. Data, analytical, and collaboration skills should be evaluated across key customer-facing organizations to identify and understand any gaps. Then, by filling in those gaps with consultants, strategic partners, and effective hiring, companies can take a solid step forward.

Consider the case of lead management. The best processes occur when technology (read IT) enables Marketing, Sales and Customer Service to augment the data associated with each lead and predictive analytical models to score those leads so that each are treated in accordance with their potential revenue impact and with the cost of the channel required to serve the lead. Specifying the data structures and underlying mathematics of the scoring model requires input from multiple organizations across the enterprise.

Technology will be at the heart of any strong move toward analytical marketing. Because of this, a forward-thinking CMO will cultivate a good strategic relationship with the CIO. In so doing, the CMO would ensure that Marketing’s business strategy is aligned with its technology strategy. This way, when sources of conflict or other roadblocks emerge, the CIO and CMO can use a broader vision to work through the issues instead of letting the process of driving positive change get derailed.

When Marketing is considering their technology strategy, they must think like a technologist and consider the following three elements in these discussions. First, will the technology (and data) be outsourced or deployed in-house. There are positives and negatives for both options and Marketing should be aware of those. Second, does Marketing want a consolidated platform for marketing management or are best-of-breed point solutions worth the additional integration costs and hassle. And third, create the technology roadmap so that all parties know where the organization is going. When Marketing is considering its technology strategy and roadmap, they would do well to consider a few points:

1. Marketing technology is not just about efficiency, it’s also about making quantum-scale improvements. Marketing should be thinking beyond simply making their existing processes more efficient, they should be architecting the interface by which Marketing interacts with the new digital world because that will shape the experiences Marketing delivers to current and potential customers.
2. The relationship between strategy and technology is circular. Marketers should avoid the trap of just buying a bunch of technology solutions and then trying to figure out a strategy to make the best use of those solutions. But crafting the strategy first and then looking for technology to fit that strategy isn’t a perfect option either. The technology industry moves at a fast pace and there are always new innovations emerging that will enable new strategies.
3. Marketers have a responsibility to understand technology. Companies are now engaging with their customers primarily through technology, and if marketing is about designing and delivering compelling customer experiences, technology is the means to accomplish that.
4. Marketing must adhere to IT governance policies as much as it does financial policies. Security, regulatory requirements, business continuity, integration with the rest of a company’s IT systems, etc., are all important facets of good technology management. Marketing cannot be a rogue organization in this process.
5. All other things being equal, IT will seek to minimize costs and risks in technology purchases. That’s a valuable insight, but only provides half of the cost/benefit equation. The benefits — an improved customer experience and better marketing performance — most of the time matters more. So, the software that is the cheapest may not be the most effective for achieving Marketing’s goals. Therefore, Marketing must have a strong enough grasp of technology to advocate for the software that best enables their organization to achieve its goals and, perhaps more importantly, to be able to apply that technology effectively when they deploy it.

Perhaps the best path is for Marketing and IT to embrace a class of hybrid professionals called marketing technologists. These professionals have with expertise in both marketing and technology, and they can fluidly combine the best of the two disciplines.

Just like the word data, the term analytics continues to grow in importance. Analytics promises to transform the way companies engage with customers, potentially delivering the kind of performance gains last seen with the proliferation of computing and the subsequent re-engineering of core processes. As companies get more savvy about employing data and analytics to make better decisions, having a robust analytics strategy will become an increasingly crucial point of competitive differentiation.

When companies are considering the role of analytics in their enterprise, it should be noted that there are four main categories of analytics tools:

1. Business Intelligence: reporting about what is happening, usually in real-time or over a defined time period. Examples: sales pipeline by product by geography; the response rate for a marketing campaign; new customer acquisitions by month; or net promoter score.
2. Descriptive Analytics: an analysis of historical data, the conventional reports and intelligent dashboards that business leaders use to better understand their business. Examples: buyer segmentation and their traits; how customers with high lifetime value differ from the average and what defines those customers.
3. Predictive Analytics: use of data from past and present, and the application of statistical models to predict (or score) what may happen in the future. Examples: who will respond to this marketing campaign; what are the potential values of each prospect in the lead database; or who will defect and when.
4. Prescriptive Analytics (or Optimization): the insights from "what if" models that tell you what to do and determines new ways to operate. Examples: what if we allocate more resources to brand building instead of call-to-action campaigns; what would be the most optimal combination of marketing spend to maximize ROI; which new product features and pricing are likely to maximize revenue.

Because data and technology will fuel analytics, it is important to identify the current capabilities of the organization and what type of analytics are needed to be a more competitive and better positioned company in this age of digital disruption. A clear vision of the desired business impact must shape the approach to defining data needs, analytical tools and organizational change.

Link to the next article in the sequence: Analytical Marketing 2.2 – Get your Data Right

Author

David Bartenwerfer is the founder and principal of Quantum Consulting and Technology. QuantumCT helps product and marketing organizations get smarter and prove, predict and optimize impact and ROI with economic and financial modeling that employs customizable algorithms and technology leading to fast and lasting insight and action. Mr. Bartenwerfer has over twenty years’ experience in the High Tech, Internet, Telecom, Media, Financial Services and Retail industries and holds a B.S. in Systems Engineering with minors in applied mathematics and economics from the University of Virginia and an M.B.A. from the Stanford Graduate School of Business. For further information, contact the author atdavidbartenwerfer@quantumct.com .