Customer insight is a prime objective for many business scenarios using Big Data. Such scenarios also provide examples of how organizations may have to change in order to reap the benefits of the “new world of data.”
Over the past several years, customer behavior, communication channels and buying mechanisms have irrevocably changed. As a result, organizations have adapted their marketing and sales techniques. The new richness of information about customers, though complex, presents the potential for deep insight that was not previously possible or feasible.
[For more details about the “new world of data” and data leadership, see the prior post “Enabling Big Insight with Big Data (Trends and Insights).”]
This post is based on an upcoming white paper called “It’s not about Big Data, it’s about Big Insight” by Achim Granzen, Architect in the Data Insights Center of Excellence, with contributions from Ken Collins, Fidan Boylu, Philip Reilly, Benjamin Wright-Jones, and Delbert Murphy.
The new age of the customer represents, more than anything, a cultural change as consumers embark to embrace continuous learning and questioning of products, services, and messages. For customers, information to make buying decisions is now ubiquitous; they are armed with sufficient information to make decisions on the spot.
For organizations targeting those consumers, it means moving away from a steady state world, driven by processes and workflows, towards a constantly changing world described by new parameters such as context, device, and location. No longer can an organization rely on a one way marketing for a fixed (and fixated) audience.
Organizations can now get a very detailed understanding of who their customers are, how they are perceiving the organizational brand and its products, and how they are comparing the brand to competitors’ brands and products by analyzing the sentiment (tone) of public posts on social media.
Using (near) real-time capabilities, an organization can “keep an ear to the ground” about the current discussions regarding brands and products. This can provide direct and very accurate feedback on the impression on a target audience of a new marketing campaign or product launch, and enable an organization to immediately know and address overtly negative perceptions and take corrective actions to protect the brand.
Nowhere is the data evolution more obvious (and indeed more progressed) than in customer insights. The change of customer perspective from record-centric to interaction-focused is even more radical. The new way of marketing in this environment requires new insight and a far more detailed understanding of the customer than ever before, being:
These are all new attributes to consider in a complex total customer view. These attributes were neither available nor captured in the traditional world of customer data.
The new world of data goes beyond classic customer analytics which primarily builds upon internal, structured data. New data allows marketing organizations creates a multi-facetted single customer view with actionable business insights from such diverse sources as Point of Sale transaction data, loyalty data, online/web experience data, lifestyle information, market research and demographic data, marketing channel response data; and specifically social media and direct communications channels.
The classic customer analytics tools such as segmentation, churn, cross-/upsell, are enriched and complemented by text and sentiment analytics, marketing and advertising analytics to enable closed loop marketing and portfolio optimization, retail behavior prediction, customer service and satisfaction improvement, and brand research/protection.
Key technology capabilities for a typical customer insights analytics solution are:
An analytics solution for customer insights will always require the combining of internal, primarily structured data, with external, primarily semi-/unstructured data in an as seamless as possible manner. A hybrid analytics platform that enables this is a core component of any architecture. External data can be processed through a cloud-based data ingestion, storage and management system, with support for streaming data. Certain analytics such as sentiment analysis can be ideally performed directly in the cloud, as it is not always required (nor feasible) to bring that data on-premises.
Internal data from CRM and ERP systems, and data warehouses, can be brought into the hybrid analytics platform either through traditional ETL, or through an Extract-Load-Transform (ELT) process, using a non-structured format as (intermediate) storage before being transferred into a (temporary) structure as needed for specific analytics purposes. Also, analytics results must be fed back into operational systems to leverage them in business processes. For example, customer churn probability and best offers must be available to all customer contact points.
To close the loop, analytics results need to be made available to the business for further exploration and visualization by business analysts, or for inclusion in management dashboards and other reporting systems.
Figure 1. Schematic Architecture including a Hybrid Analytics Platform
When it comes to obtaining new customer insights, the possibilities and promises of exploiting social media are almost always at the forefront of an organization’s action plan. In the excitement over the seemingly endless possibilities for social media analytics it is of utmost importance to understand how social media works, and how it can be utilized. More than anything, this requires a social media strategy.
Promote the brand with social media and sentiment analytics
Contoso is struggling to deal with changing customer behavior and eroding brand perception. Competition from new market entrants presents additional challenges as they push new innovations, offer lower prices, and provide better overall customer experiences.
By tapping into the vast amounts of information generated through social media and connected devices, Contoso finds new opportunities to better understand their customer’s preferences and perceptions of their brand. The social data is easily combined with internal market data to gain deeper insights into brand awareness and profitable customer segments.
Targeting an organization’s existing customer base to drive additional revenue is a major objective for any marketing operations team. Improving the return of any marketing campaign, increasing the wallet share and tapping into new markets have always been and will remain top priorities.
Support better business and sales decision-making
Contoso is struggling to provide their business users with a unified view of all internal customer information: the services, sales and marketing teams have to struggle to assemble required information from a multitude of systems.
Using a consolidation approach across internal as well as external customer data, the Contoso IT organization is providing their business users with a 360-degree customer view for key business applications. Services, sales and marketing teams are able to react quickly and decisively in their daily business and sales decision making, as they have an accurate view of all customer information available to them at any time.
Implement effective customer segmentation to create targeted campaigns
Contoso is struggling to identify the main segments of their customer base accurately. As a consequence, they are not able to create targeted marketing campaigns, or place successful cross-/up-sell offers with their most profitable customers. A one-size-fits-all approach is increasingly responsible for a bad brand image.
By employing descriptive analytics, Contoso marketing professionals are able to identify and understand their customer segments better, and create specific offerings and campaigns targeted at each segment, which results in a much higher response rate and increased brand perception, as customers “feel understood” by Contoso.
Increase market and wallet share using predictive customer analytics
Contoso is struggling to increase their market and wallet share, and is unsuccessful in creating high return marketing campaigns. Competition from new market entrants presents additional challenges and lure once loyal customers away, resulting in an erosion of Contoso’s customer base.
By employing predictive analytics on their aggregated customer data, Contoso is able to understand customer preferences, intentions of changing brands, customer lifetime value; and discover the highest yield route to driving market and wallet share. The internal data is easily augmented with external data to gain deeper insights into customer preferences and profitable segments.
Despite the trends towards new channels and means of interactions between an organization and its customers, the traditional call-center is nowhere near to becoming obsolete. On the contrary: businesses are offering more and more services without an actual physical or local presence (for example, car insurance), making call centers a primary means of communication for these online services, as well as for traditional businesses shifting services to a lower cost mode of operation.
Maximize customer retention and satisfaction through improved service
Contoso Call Center is challenged by the inability of agents to access all relevant customer information when in a call, which results in a low customer satisfaction and cancellation of services. Contoso needs to capture data from customer service interactions and provide real-time customer insight to help call center staff deliver high quality service experiences to customers.
Employing an analytics solution within their call center operations, Contoso can now provide call center staff with a complete view of the customer in real-time. This leads to an overall improvement of the quality of their customer service, with agents having at their fingertips a combined view of customer preferences and profitability. Contoso call center operations can also capture and integrate call center performance data at the individual customer level, including call volume, call duration, and resolution status to identify past performance and take corrective action where necessary to continuously improve the customer experience.