Analytic Strategies for a Data-Driven Customer Experience
The data-driven customer experience is critical to the future growth and development of organizations, particularly in today’s hyper-competitive economy. There’s a prevailing understanding that opening up data and sharing it with customers will go a long way to advancing that customer experience. Things like predictive analytics and prescriptive analytics can also hold up the promise of radical change in the way organizations do business and then there are skill sets and obstacles to consider as you build your analytics story. Let's look at how these aspects can help.
Data-Driven Customer Experience
Improving customer experience is every marketer’s goal in today’s connected world. At its core is data, which we gather to better understand any interaction we have with our prospects and customers.
Companies nowadays actually map and produce marketing content through–ideally–interactive assets to each stage of their buyer’s journey. Smart marketers even include micro-surveying at different moments to gather more data at every touch point.
Moving forward, we see data-driven customer experience rapidly evolving, taking advantage of Artificial Intelligence and Machine Learning (AI/ML) algorithms alongside analytics and data visualization platforms. These technologies allow people to not just interpret the growing volumes of data better, but fine-tune relevant, personalized offers based on changing customer behavior. We will also be able to predict what a customer will need and respond in more efficient ways; as well as run many more concurrent tests of different offers or configurations of products and services with near-real time results that allow us to adapt to market demand.
It’s no wonder why analyst firm IDC states that by 2020, organizations that analyze all relevant data and deliver actionable information will achieve an extra $430 billion in productivity gains over their less analytically-oriented peers.
Opening up Data for Advancing Customer Experience
Nowadays, we expect most people to educate themselves online or though mobile apps reading peer reviews, assessing a vendor’s reputation, and evaluating alternatives before they even contact the vendor to initiate a purchase. Sharing meaningful information doesn’t just improve your data-driven customer experience, it can be a differentiator. Furthermore, you can’t really personalize that experience without exchanging data with prospects. Strategies for a “next best offer/action” can’t be fully implemented without engaging your audience interactive manner to understand preferences and adjust to them.
For example, one of our large global airline customers created a solution for optimizing the web and kiosk check-in experience. It allows passengers to upgrade seats, buy miles, and select promotions. The price for these offers is very dynamic based on many variables, such as the customer’s profile; past preferences; aircraft capacity; number of booked seats, other factors previously known about the passengers, and captured during the interactive check-in process. The solution provides a recommendation on what the passenger is most likely to accept based on information captured on the spot. Other things are personalized as well, such as the layout of the offers; how many offers are shown; and direct emails.
Self-Service Data Preparation
Data integration is essential for accessing the right data sources for analytical purposes. But traditional data integration usually performed by IT or data engineers is now complemented with “distributed data preparation” or “self-service data preparation,” which is typically done by business analysts or data scientists.
It’s no wonder why analyst firm IDC states that by 2020, organizations that analyze all relevant data and deliver actionable information will achieve an extra $430 billion in productivity gains
Gartner predicts that by 2020, self-service data preparation tools will be used in more than 50 percent new data integration efforts for analytics. Both traditional and distributed are necessary, but their mix is highly dependent on the nature of analytic needs and projects. While there are many factors, it mostly depends on the amount of time required to access and cure data, in addition to the data types, the sources and targets, and whether or not a data model is required and desired.
Today people seek to visually explore and discover facts and insights from their data as they go along mashing up various sources, analyzing them on-the-fly with data visualization tools and techniques, “because I’ll know it when I see it.”But visual data discovery isn’t the only case for a combined data integration / data preparation mix. The need is arguably greater for more advanced prescriptive or predictive analysis cases, and when the necessary human judgment over ML recommendations is applied.
Biggest Obstacles to Access Data
Most obstacles toward a data-driven customer experience are related to access to data and timeliness of its analysis–which in part is a reason for the proliferation of AI/ML technologies. With the rapid increase in computational capacity and an abundance of data coming from so many new sources including smart sensors and robotics, the need for speed is obviously greater.
But there are two components to this. First, being able to access all relevant sources to support faster, more agile business models is vital. This is the case for a mix of traditional data integration and distributed data preparation that we discussed earlier. It’s not just about combining all types of data sources, but allowing people to prepare it in ways that can meet their analysis timeframes.
The second part is about having a more efficient way to visually analyze, understand, and interpret those data. Today this is not anymore just about doing it quickly, but having the ability to shift focus of analyses–and the respective mashed data sources–at any point, depending on changing market conditions, or new decisions /strategies stemming from the discoveries of the analysis.
Skills to Implement Adequate Strategies
Technology leaders today are required to build a diverse set of skills in their organizations that aren’t just based on technical experience or business acumen, but related to strategy and data management, as we quickly move into a world of analytics-as-a-service.
From a data perspective, successful IT leaders differentiate by treating data as a valuable corporate asset. This implies understanding the business and financial aspects of data-as-an-asset, as well as being able to formulate and implement a corporate data strategy.
Alongside, data preparation, data wrangling, and data modeling are necessary, particularly for assessing the mix of traditional data integration and distributed data preparation within a corporate data strategy.
From an analytics standpoint, we have the infrastructure and deployment side, where Technology leaders need the skills to implement adequate hybrid strategies for their analytics platforms. This implies understanding how data repositories/ sources, analytics platforms, or a combination of both can remain on-premises or be deployed on the cloud, and how will this evolve in time–essential guidance for the organization’s journey to the cloud.
The other aspect of analytics relates to building and fostering an analytical culture in the organization. The required skills include analytic process design, data visualization, predictive analytics, scenario modeling and what-if analysis, and perhaps one of the key elements of an analytical culture, storytelling with data.
Future Technology Innovation
Analytics is evolving, gradually incorporating AI / ML technologies and algorithms into its fabric, resulting in what we call “adaptive intelligence.” This complements analytics strategies and can support data-driven customer experience.
This is of interest to me because adaptive intelligence is at the intersection of people judgment and machine automation. While machines can ingest more data in one second than what people can in ten years without forgetting it and without fatigue; and automation greatly simplifies repetitive computational deductive or inductive processes, we cannot replace human reasoning. The ability to understand and adjust analytic model inputs and training data, improve data imperfections, and apply ethics to our use and interpretation of data are a few examples of what machines can’t completely replace.