“Without data, you’re just another person with an opinion.” ― W. Edwards Demming
According to the 2021 CMO Survey, 72% of marketers believe that marketing’s role has increased in importance because of the pandemic. And despite a slight decrease in marketing spending over the last year, overall marketing expenditures are expected to grow by 14.3% — with digital spend growing the most at 10.1%.
Customer experience (CX) spending however is down from its high in June of 2020 (16.7%) to 14.4% earlier this year. The overall decrease in CX budget comes despite these survey findings: “across all industries, consensus shows all customers demand a strong customer experience catapulting it into the #1 priority for marketers.”
Findings from Accenture’s technology vision report on the I in Experience back up the need for continued focus on CX, with 67% of consumers indicating that it is important for companies to customize content automatically based their current context. To do this Accenture highlights the need for a “forensic-level” understanding of customers with the ability to answer questions such as these:
- Which devices are they using and when?
- How have their preferences changed over time?
- How has the context shaping those preferences also changed?
So how do marketers reconcile the increasing customer demand for CX with decreasing CX expenditure? It starts with how we use data. Marketers certainly have the data today to forge forensic-level customer understanding and contextual content customization, but many continue to fall short of customer expectations. According to a Forrester report titled “When Data Drags You Down,” the suboptimal application of data is to blame, stemming from marketers’ misconceptions about their data use.
Diving Into the Customer Data
The good news is marketers know they need to make better use of data. In the Forrester report, three of the top five marketing priorities for 2021 revolved around data:
- Improve the quality of customer data.
- Improve use of data & analytics.
- Improve lead/opportunity quality (tangentially related to better application of customer data).
However, the rankings for types of data used to gain better customer insight shows there is some growing to do from a data maturity perspective.
- Marketing responses — 45%
- Identity — 43%
- Transaction data — 42%
- Demographic data — 42%
- Social media content — 41%
- Technology ownership/use — 41%
- Application — 35%
- Sentiment — 34%
- Preference data — 32%
- Journey/activation — 26%
- Situation — 26%
My thoughts on data maturity come primarily from a few interesting aspects of this list, both rank order and what is missing. First, it is surprising that web and mobile behavior data are not specifically called out. While it may be included in technology use and application content, or in the journey activation category, even these are pretty far down the list considering the CMO survey’s finding that digital spending is the fastest growing category in the marketing budget. In fact — in a study of the factors impacting CX, Futurum Research found that 76% of marketers believe consumers will stick with the pandemic-induced hybrid digital/physical engagement model and 66% are accelerating their tracking of online behaviors because of this.
Related Article: Data-Driven Decisions Need Context
The Data You Need to Provide Context
When you compare these data rankings to the types of context that can be gleaned from customer data it is curious that beacon, sensor and location data did not make the list and sentiment, preference and situation data are at the bottom.
I have written about context before, but it bears repeating, particularly when you consider Accenture’s positioning of the need for contextual reactions.
Personal context provides great insight into what motivates an individual and is invaluable in matching content, channel options and products to what the customer actually values. Personal context adds a level of sophistication to personalization efforts that simply can’t be gleaned by transaction, demographic and marketing response data alone. In addition to social media activity, sentiment and preference data are key to uncovering this type of context.
Situational context is arguably the most critical when putting the “I back in experience.” This includes web and mobile activity, location and GPS services, beacon and sensor information as well as the situation data ranked least used in the Forrester report. If captured and applied in real time, this data can illuminate moments that matter: What is the customer is doing at this moment, and is there something they need that we can provide?
Related Article: Leap Into the Future: Shape Customer Journeys With Context
Optimizing the Customer Engagement Cycle
There are four distinct stages in the customer engagement cycle, each with its own set of data and analytics capabilities that should be optimized.
Listening is where the current context for a customer can be gleaned. Listening includes asking questions like: what are they doing? Where are they (channel and location)? Are they doing anything different that indicates a new need, and do we even need to respond to what we are seeing?
Critical capabilities (including data) needed in the listen stage include the ability to access data about transactions and interactions, the ability to detect opinions and sentiment, the ability to identify key events and signals from the data, and the ability to stream the data so it can be accessed and used in real-time if the situation warrants.
Understanding is where marketers can paint a bigger picture around the point in time data gleaned from listening. Questions answered through additional data include the following: Who is this really? Do we know them or are they new to us? What do we know about them? What can we determine that they really need?
Critical capabilities in understand include identity management to tell us exactly who we are talking to, incorporating history and relationship context with the listen data in order to determine what they have done with us in the past, and the ability to combine all of this with the real-time context needed to paint a complete picture.
Decide is where options for response are identified and evaluated. The questions we are trying to answer in decide include what are the response options? What is best for the customer? Is there a way to satisfy the customer and still meet our goals?
Decide differs from the first two stages in that the capabilities transition from primarily gathering and understanding data into the technology and analytics that are applied to that data. This includes the ability to combine sophisticated business rules with predictive analytics, the ability to conduct robust test and learn methodologies to optimization efforts, and the ability to provide guided analytics for marketers without access to data scientists.
Act, the final stage in the cycle, is where the decisions are seamlessly embedded into the distribution channels, offers are made, and results are recorded. It is particularly important to record results that indicate offer relevance and customer reactions.
As with decide, the capabilities here are primarily technical. They include deploying personalized and optimized recommendations across all channels, and the ability to follow the customer across the entire journey, reinforcing or modifying the messages as the journey dictates and feeding the journey information back into the engagement cycle.
The bottom line on data-driven journey optimization? Use all of the data available and make sure you understand both the data and capabilities needed at each stage of the customer engagement cycle.
Lisa Loftis is a thought leader on the SAS Best Practices team, where she focuses on customer intelligence, customer experience management and digital marketing. She is co-author of the book, Building the Customer Centric Enterprise.