Big data analytics in marketing

From hype to real help: Finding valuable consumer insight in a stream of data.

By Michael Svilar, Arnab Chakraborty and Athina Kanioura

Big data analytics in marketing

Digital consumers are connected all the time, through their smartphones, tablets, gaming consoles and pretty much every application, service and channel accessible through these devices. As they move among devices and channels, they are creating multiple customer touch-points across different mediums – online, offline, proprietary, third party, corporate networks, social networks, location-based and mobile. To marketers this information presents a great opportunity to better target their consumers. Retailers have adopted advanced analytics such as operations research to provide personalized recommendations to their consumers online. Telecommunications providers use big data techniques to reduce customer churn. Retail banks use big data analytics for fraud prevention.

The new context of data around customers

To a large extent, big data refers to the ever-increasing data deluge in terms of volume, variety, velocity and complexity that is being generated in today’s digital eco-system. As shown in Figure 1, big data sets are generated around customers based on their online purchases, web clicks, social media activities, smart connected devices, geo-location, etc. Customers create new data at every step they take, be it structured data when clicking through websites or unstructured data when posting comments on Facebook. Using big data technologies and analytics methods, marketers can mine, combine and analyze both types of data in near real time. This can help them discover hidden patterns such as the way different groups of customers interact and how this leads to purchase decisions. Equipped with these insights, companies can then develop targeted marketing campaigns that cater to the customer’s individual preferences.

Figure 1: Next generation customer profile

Figure 1: Next generation customer profile.

Advancements in technology such as in-memory computing and the rise of open source massive parallel platforms are further driving opportunities to harness big data sets. These big data platforms facilitate rapid data ingestion in a cost-effective manner and enable real-time data analysis.

Big Data = Big Opportunity

No wonder that harnessing the potential of big data is on the agenda of chief marketing officers in almost every large company. When and how should they tap into big data sets and what should they do with it? What is the best approach to realize the benefits? What are the opportunities and challenges? In particular, marketing leaders want to know how to monetize the big data.

Sophisticated analytics solutions for big data provide new approaches to addressing some of the key marketing imperatives and delivering impressive results. These solutions can transform traditional marketing roles and improve how to execute essential marketing functions. Marketers are collecting the data produced from a variety of live customer touch-points to paint a complete picture of each customer’s behavior. Analyzing this large amount of data in motion enables marketers to fine-tune customer segmentation models and apply the insights to develop customer engagement strategies and improve the value of customer interactions.

As the number of customer channels increases, marketers need to ensure that they are delivering a tailored experience across all channels. All of these efforts help provide a highly personalized experience while maximizing the return on the marketing investment. In the longer term, marketers can feed these new, real-time insights back into the organization to influence product development and product pricing as well.

From Big Data to Big Marketing Outcomes

Multiple big data applications are showing tremendous potential for driving marketing impact in the customer management domain. The following examples illustrate the various applications.

1. Next best action to engage customers. Next best action (NBA) marketing is a customer-centric marketing approach that considers in real time all potential offers for each individual customer and then determines the optimal one. The next best action offer is determined by the customer’s interests and needs as well as by the marketing organization’s business objectives, policies and regulations. This is in sharp contrast to traditional marketing approaches – to create an offer for a product or service first and then attempt to find interested and eligible prospects.

Next best action is enabled through the use of real-time decisioning technology that leverages call center data, transaction data, customer information and a set of business rules to determine the one or many offers for which the customer is eligible for at the moment of interaction. These are prioritized and optimized to propose the best offer to the customer. Prioritization is driven by an algorithm that combines advanced analytics (that computes the customer’s propensity to accept the offer) and complex business rules (that determine the treatment logic). In addition, big data sources such as social media and click-stream can be used to significantly boost the predictability of the analytical models.

As shown in Figure 2, what makes big data analytics even more powerful is that in many cases it enables businesses to monitor events in real-time and embed insights in real-time decision-making. This enables contextual, personalized and dynamic decision recommendations to customers across channels.

Figure 2: Accenture’s next generation analytics conceptual technical architecture

Figure 2: Accenture’s next generation analytics conceptual technical architecture.

2. Personalization of online shopping. Two decades ago the retail industry changed radically with the appearance of online retailers that used the Internet to expand their market reach and at the same time reduce inventory, personnel and operational costs. Today, it further advances shopping by converting it to a personalized experience through collecting and processing vast amounts of data characterized by volume, variety, velocity and complexity. Online retailers use powerful big data systems to gather information about user preferences, user browsing and purchasing behavior, product attributes, geographic location of purchases, inventory levels, active promotions and campaigns and anything else that can be digitally recorded. These data sources, which increase by several terabytes on a daily basis, are converted into information and insights by intelligent machine-learning algorithms, which identify customer interests and product affinities, trace geographic peculiarities and identify seasonal effects among others and thus predict the current and future needs of customers. This information is then used to provide a personalized experience to customers by illustrating the items of interest, recommending most likely offers, and helping customers find what they want and when they want it at the best possible price. Personalized sites save time for customers and increase their satisfaction along with the bottom line of online retailers. Big data has changed the name of the game in online shopping.

3. Monetizing big data for targeted dynamic advertisement. Data monetization creates opportunities for organizations with significant data volumes to leverage untapped or under-tapped information and create new sources of revenue. As shown in Figure 3, a number of forces are converging to create conditions ripe for data monetization. The volume and richness of the data now uniquely accessible to mobile providers – whether in the form of transactions, inquiries, text messages or tweets, GPS locations or live video feeds – offers a veritable gold mine of insights and applications. And even as mobile phones have become the primary device through which consumers get their information, those very same devices have begun to facilitate new types of information, including extremely precise, real-time, geo-location information.

Figure 3: Data monetization.

Figure 3: Data monetization.

Mobile network operators, which sit on large amounts of customer data, have a unique opportunity to monetize the data they are gathering about their customers. Given their direct relationships with customers, they are likely to have the most accurate and complete customer information. They can generate analytics-driven behavioral insights based on mobile engagement, location and demographics information, creating a 360-degree view of the consumer. For outdoor advertisers, for example, such insights can measure the effectiveness of outdoor advertising units, validating the impact and reach of specific ad campaigns. Outdoor advertisement companies can have access to insights about the habits of the audiences they want to reach and the locations at which they can best reach them. Data helps advertisers turn every billboard into a targeted entity that reaches the right audience in the right place at the right time, ultimately breathing new life into what were previously considered “dead boards.”

4. Machine-to-machine (M2M) analytics to improve product life-cycle management. There has been a tremendous advancement in sensor technology that goes in machines, automobiles, mobile devices, utility grids and enterprise networks. This has led to the generation of machine-to-machine (M2M) data at an unprecedented rate and in real time. Companies can use the data emitted by sensors from a wide variety of applications to analyze and improve efficiency of manufacturing processes, predict device failures and identify opportune time to upsell new products to customer. The data can also provide insights for product development, customer support and sales teams who use the information to, for example, improve product features, increase revenues and lower costs.

The big data exhaust coming from sensors in devices represents a huge gold mine for data scientist to discover hidden patterns and provide deep insights that can benefit businesses, government and overall society. Companies are using advance statistical modeling techniques to analyze the sensor data and provide real-time insights on event correlations, root cause analysis, forecast potential risks and visualize possible scenarios. This can be applied for large enterprise networks to analyze the machine log data coming from various network devices in real time to predict which network devices have a higher propensity to fail and identify potential network outages in advance and hence initiate pro-active remediation actions to enhance customer service levels and experience. Another important application is to monitor the usage of devices and products by customers and provide pro-active alerts and triggers to the sales team on the right time to contact the customer for a product upgrade/ refresh. This can be very effective to build a 1-2-1 relationship with the customer and help cross-sell and up-sell to the customer.

Key Considerations for a Big Data Journey

Big data analytics is a journey that helps organizations solve key business issues by converting data into insights to influence business actions and drive critical business outcomes. As companies try to take advantage of the big data opportunity, they need not be overwhelmed by the various challenges that might await them.

Marketing leaders will need to start their journey by asking some important questions to maximize the ROI from their big data investment:

  • What should our big data analytics roadmap look like to achieve our marketing objectives?
  • What business outcomes would we like to influence by leveraging big data around customers?
  • What capabilities and services should we develop by leveraging big data that enable a strong competitive advantage?
  • What technology options will enable our big data analytics journey?
  • Do we have the appropriate skills and resources in-house to embark on the big data journey?

The opportunities from big data are vast and will largely depend on the vision and leadership provided by the senior leaders in the organization. Senior leaders should enable the culture of experimentation and learning by driving important pilots and point of contacts using big data. Based on the small successes, these will pave the way for large-scale adoption of big data approaches in marketing, making it more innovative and impactful to customers.

Michael Svilar is global managing director, Client Services and Global Lead, Accenture Analytics; Arnab Chakraborty (arnab.d.chakraborty@accenture.com) is a managing director – Industry Solutions and Big Data Analytics Lead, Accenture Analytics; Athina Kanioura is a managing director – Functional Analytics Lead, Accenture Analytics.