Customer relationship marketing, non-personal promotion, and direct-to-consumer are all concepts in marketing that aim to create the most important communication with your customer. Whether the communication happens on the phone, on the Internet or in the form of direct mail and/or email, the interaction with your customer or prospect should be informed by customer intelligence. The practice of building customer acumen into every interaction, and listening to the response so the next communication can keep getting better isn’t at the core of intelligent use of big data — it is big data.
We talk about big data, and its use on new response channels that can be stored in our database to help us understand not only what the value of the customers is, but also what part of the customer lifecycle to target, and what is that customer’s significance is to our business. These bits of found knowledge are important insights that can be made usable by marketing analytics as well as any type of strategy, and they really do accrue value over time.
Building a roadmap for including big data is a step-by-step process.
Big Data Creates the Customer Journey Roadmap
1. Building Informational Assets With Strategic Discovery
Often the first engagement is to develop some customer-focused and market-focused analytical benchmarks that can be used to help make decisions about new marketing campaigns and, more often than not, help forecast ROI for each campaign. Many of us don’t have the time to look or simply don’t want to find insights that can come from a 360° view of our customers. We are looking for uniqueness such as customer lifecycle stages and customer value or segment. This strategic process begins to answer questions about customers that can drive our multi-channel campaign’s design and how we measure the success of it.
2. Segmentation—Giving Your Customers the Attention They Need
Normally, a segmentation system is designed to be helpful in driving messaging tone and focus while identifying the proper message to deliver. Segments should have the correct classification by one or more characteristics in order to realize which of your customers will need what type of attention. The perfect segment should meet specific standards:
It’s internally harmonized
It’s externally harmonized
The customer responded similarly
It can be reached uniformly
3. Campaign Customering, Testing and Analysis
Each campaign plan team needs to establish a clear method for campaign customering and testing for maximizing results. We need to have a mandate to check the boxes on each of these components:
The campaign design should include a consistent “test and learn" approach that can be carried out from one campaign to the next with new learning goals building upon findings from previous campaigns. Add to this a method for building a business case for each campaign to predict ROI and help with prioritization of the campaign changes.
When the customering and testing method for each campaign is recognized, make sure to carefully document this process for potential reproduction.
Develop a protocol for predictive analytics for each campaign—whether models will be created for the pilot phase, or be built on results for future stages of campaign development.
Of course each campaign needs an established methodology for back-end campaign analysis—which will be documented for future use and roll-out.
Establish best practices of reporting on campaigns—different types of reports for different levels of management are usually required, and this practice would be established early on in the campaign design process.
4. Integrate Transactions for
Response Management
As marketers seek to embrace customer engagement, their presence takes on singular importance. Multi-channel marketers need to examine how to bring direct marketing and web activity more closely together for:
Fulfilling customers’ needs by providing immediate messages relevant to them on a personal level.
Directly measuring personally identifiable conversion results from campaigns that cannot be easily achieved through traditional methods, such as non-personal promotion or direct-to-consumer advertisements.
5. Identify Opportunities for Impactful Insights
We normally use survey methods both to collect critical data needed to drive multi-channel marketing programs/campaigns and to build predictive analytics.
Evaluate whether there is data you wished you had for campaigns but that is not available from any source
Behavioral surveys with compound analyses are highly useful for identifying the features and proper mix for plans as well as prices that consumers are willing to pay for those features.
Determine if there is a proof of concept for the use of primary research to devise customer strategies and campaign design.
Now that we know how we intend to use big data through our roadmap — especially with those large, expanding, and multi-form datasets, we must find the right type of insight clusters that allow us to imply much more than large volume of dataset but also look for specific that data clusters that have three key attributes:
Volume (large datasets)
Velocity (speed of generation)
Variety (combination of structured and unstructured data)
These three attributes characterize the data from social media, video, third parties, web applications, and main and mobile devices, and these differentiate big data from the large structured data organizations have dealt with hitherto. In many instances, standard structured data are now joined by unstructured data, such as text and language as well as RSS and XML.
Big Conversations
Big data has become a big topic. The conversations, buzz, articles, blogs, conferences, consultancies, and technologies have spiked in recent years. A search of the term “big data" on any search engine, which serves as a good guide for social interest, shows a strikingly astronomic growth over the past 12 months. The velocity of big data conversation buzz and searches compares to that of social media in late 2008 through 2010.
Because of the larger and more diverse nature of information contained in big data, such a dataset could theoretically hold a larger set of insights that could translate into major wins for businesses and brands.
For example, a dataset rich in consumer perceptual and behavioral footprints should allow firms to answer hitherto impossible questions and translate into significant competitive wins. Big data benefits can be grouped into three broad categories: consumer insights, organization intelligence, and operational benefits:
Better consumer insights
Better contextual intelligence
Improved internal operational processes
Despite the big data buzz and its benefits, is a focus on big data the right conversation for building data analytics credibility and strategic relevance? One can predict with some certainty that big data is a temporary hype that will limp and fade away: too data-focused to matter in the long run. How exiting are the terms “customer level data," “cookie data," “large data sets," or “patient level data" today? These are mere descriptors. Worthy, lasting and relevant ideas are those that pertain to an idea, a concept, or an approach, like customer relationship management, multichannel (which we have our roadmap for) optimization, or competing on analytics.
While big data could deliver substantial benefits, and help keep data and analytics in our roadmap, the conversations are better positioned under the larger umbrella of advanced data analytics or gaining competitive advantage through analytics; that the data-focused conversation may erode the gains made over the past years in the area of elevating analytics to a strategic and competitive leverage. The conversation should not be about the data, but what we do with it or get from it. The data focus may further alienate businesses that still struggle with making the best of basic datasets for meaningful insights.
Like other in-the-moment topics that may have strong underlying value but are easily hijacked by trendy perspectives, attention on big data has not adequately focused on the underlying time-proven philosophy of data being valued for actionable insights.
The key to success with big data is intertwining all of the mentioned channels, such as our roadmap and different types of structured and unstructured big data. In order to compliment the standard channels of communication, it is vital to take advantage of forward-thinking big data use. Our customer journey channels and big data clusters all play an important part in successful outreach. Being able to react to a customer who has something positive or negative to say about an experience is invaluable. Keeping track of our big data clusters who are responsive to tactics provides the ability to communicate with a certain level of sensitivity by avoiding unwanted outreach.
It is the evolution of communication mixed with technology that allows the days of “spray and pray" marketing to be over. It is time to deliver relevant content of interest to HCPs at the time and channel of their preference. This allows the ultimate goal to be accomplished, which is an open and meaningful dialogue.
To fully simplify the definition of how we’d truly want to use big data to our marketing advantage, I would say something to the effect of, “Big data allows us the personalization of a collaborative conversation with optimized content delivery by proper vehicle within our customer journey."
Big data we need to look at and use should be:
Collaborative, leveraging both responsive and non-responsive clusters to create an ongoing and interactive conversation. Collaborative enables the learning aspect of personalization and aids in the targeting of the decision management engine. Conversational, recognizing that personalization is a two-way dialogue.
Conversations are about listening and responding accordingly, which will ultimately feed our big data clusters. Conversations occur across the entire addressability spectrum and within an integrated environment of channel and media, the nature of the conversations become very data-rich and allow us to insight-mine on how to better communicate. Enhance is the ability to combine many varieties of data types to produce useful models. Enhance data models leverage the appropriate content so as to identify key “pivot points." The key purpose of the pivot points is to provide relevant messages and offers with the highest likelihood of effecting the consumer conversation.
Significant represents the part of personalization that speaks directly to the individual. It identifies the person by name.
It leverages both explicit and implicit data to present the customer with content and creative that speaks to them directly. Customer journey recognizes that a relationship with a customer should exist beyond a linear funnel; instead, personalization should focus on the development of an ongoing relationship between the company and their consumers. Journey is what drives consumers from being prospects to brand advocates.
By creating a checklist of these stops on your big data roadmap, you can incorporate your customer intelligence into all of your marketing efforts and deliver greater relevance, better results, and promise a constant ROI. (PV)
Ogilvy CommonHealth Worldwide — the health behavior specialists of Ogilvy & Mather — is committed to creativity and effectiveness in healthcare communications, everywhere.
For more information, visit ogilvychww.com
