Big data and analytics for a holistic customer journey
The most common question from any business shopping for enterprise software is – “what about reporting and analytics”? At the end of the day whether you have the latest and greatest Customer Engagement solution or if you are stuck with legacy or siloed customer engagement systems it is all about being able to get to the data, understand it and be able to connect it across systems for analysis. Without that capability you will not be able to achieve the largest ROI potential in making good business decisions for your company.
As we all know in the modern business world, customer experience is everything. Digital marketing, sales and customer service focuses on ensuring a seamless, holistic, multi-channel, cross-channel and omni-channel experience to all customers all the time. This is the pivotal concept of a “holistic customer journey.”
Big data and agile analytics are key tools to help your company personalize and target the delivery of superior experiences to all customers. And being able to include activity and behavior of self-service and online interactions in the sales catalog or shopping cart is a key part of those interactions that should not be overlooked.
Empowering your call center to optimize omni-channel experiences
Customer expectations on customer service are higher than ever as customers can interact with a business in multiple touchpoints. Whether your customers research a service offering, purchase a product, or conduct self-service, they rely on your digital channels to conduct business. Whenever customers have more complex requests or transactions, they contact the call center and expect immediate answers. Contact centers play a central part, more than ever, by offering optimal customer experience. Customer service representatives (CSRs) are at the front of the business responding to customer requests from multiple online channels. More integral to your overall business, visibility into the online channels becomes more essential to your success.
The business stakes could not be higher for smart customer engagement based on good data analytics. In an experience-driven economy, your customers will quickly leave you if you don’t offer an experience that’s tailored to their ever-changing life circumstances. To re-align your customer-engagement initiatives around this new reality, you will need a clear roadmap for where to start and how to proceed.
Overcoming big data challenges for insight and opportunity
Consumer attention is shifting from traditional channels such as TV, radio and print media to digital media channels and devices. A study by Forrester Research found 50 percent of US consumers’ time was spent online, with thousands of new digital devices competing for consumer attention. Marketers have to maneuver their way through transactions and click-streams; call-center interactions, e-mail and retail store visits; product and service feedback and social media channels.
The potential to leverage big data is unlimited. Each industry has its own unique challenges that can benefit from using big data for new insights and improved decision-making. The fol-lowing five cross-industry use cases provide excellent starting points for anyone looking to begin their big data journey.
This “big data”—the enormous volume, variety and velocity of data being produced—holds tremendous potential for marketing professionals to gain unprecedented insights about consumers. Insights derived from big data analytics will drive future decisions in accurately delivering the right message to the right person at the right time at the right price for maximized customer value.The wealth of big data can be used to create win-win scenarios in which insights are turned into relevance as customers prog-ress through the purchase funnel. Yet the volume, velocity and variety of big data also comes with numerous challenges defined by questions about how best to access, analyze, optimize and apply insights to innovate customer lifecycles and create differentiated experiences:
- Are we acquiring, aggregating, and analyzing the right data sources?
- Does fine-grained customer segmentation and influence assessment truly help us to target our campaigns for maximum lift?
- Do proactive big-data-powered target marketing and engagement efforts expose us to charges that we’re invading privacy and stalking the customer?
- Are we going too far or not far enough to bring social, mobile, and other digital channels into the core of our customer engagement strategies?
- Should we be incorporating real-time click-stream analytics and other behavioral data sources into efforts to tune the customer experience across multiple channels?
- Are we truly differentiating with all of these digital engagement efforts, or simply keeping up with the competition?
How to acquire, grow and retain customers
Do your competitors have more insight about your customers than you do? Are you effectively converting your audience insights into added value for your consumers? The following four objectives can help you can acquire, grow, and retain customers by improving customer interaction, building long term relationships and realizing value from customer sentiment.
- Personalization—Ensure each customer interaction is unique and tailored to buying journey by predicting best communi-cation method, channel, message, and time of delivery.
- Profitability—Enhance a customer’s lifetime value through advanced association methods that deliver targeted up/cross-sell offers in real-time and optimize use of marketing resources.
- Retention—Increase retention and customer satisfaction by detecting anomalies in desired behavior through sentiment analysis and scoring to proactively make tailored offers.
- Acquisition—Improve accuracy and response to marketing campaigns, reduce acquisition costs and predict lifetime value using granular micro segments based on profitable customers.
Delivering a superior customer experience
Predictive analytics captures unstructured and structured data, uncovers hidden patterns and associations within that data to determine future outcomes, and acts upon the insights gained through optimized, real-time decision making.
The following three objectives of customer analytics can help your organization gain deep understanding into customer attitudes and preferences to predict future behavior and deliver a 360-degree view of the customer throughout their life cycle:
Obtaining the right data on your customer. This includes data on customer sentiment, customer experiences and feelings as well as inclinations and predispositions. The best place to find such information is through social media. The challenge for marketers is how to harvest that intelligence and bring it into a big data platform and correlate it with all the historical data on customer buying patterns etc. There are many additional, typically siloed, sources of data, such as mobile and geospatial, to combine with customer records to monitor behavior and drive rich intelligence into predictive models.
Process, store and manage data. Unstructured sources of data are rich and intelligent. Marketers need to process, store them and manage these types of challenging data sources in a coherent way. Scalability and processing power is key as you bring in more sources of structured and unstructured customer data. You want the ability to enhance that historical and real time record of your customer as your ability to appropriately apply that data grows to add value along the purchase funnel.
Build predictive models. Data scientists who are essentially statistical and predictive modellers can help with complex tasks including customer marketing and churn analysis. These experts can help build and tune these models from all of this data. Therefore, you need to train and cross-train existing data modellers in new approaches that are fundamental to marketing campaign analysis, such as MapReduce and R, text analytics and more to visualize patterns and acquire, grow and retain customers through personalization, profitability, retention and acquisition.
Driving customer interactions
By asking how, why, who and what, questions requiring multiple systems, a fusion is achieved that enables deeper insights:
- What products can I up-sell this customer?
- What impact will inventory have on this customer?
- What marketing materials or “next-best-offer” should I send?
- What should I know before contacting for renewal?
- What’s going on with this customer today?
- How can we increase engagement?
- How can we get more customers like this person?
By bringing disparate data sources together to create a single view of each customer, companies start to see increased customer satisfaction and decreased customer churn. When enterprise data is brought together with unstructured data for real-time predictive and social analytics, there are benefits along the content delivery value chain. This includes a deep understanding of audience sentiment, as well as the ability to anticipate customer behavior and offer real time incentives to accelerate offer acceptance and overall conversions.
Conclusion
Customers won’t wait for you to upload the nightly batch of fresh intelligence for new offers. Your business needs a real-time, 360-degree view of the world through the customer’s eyes that is updated moment-to-moment. Throughout the world, marketing and digital media organizations face a growing variety of analytics tools while also facing a critical shortage of analytical skills. Big data effectiveness hinges on addressing this significant gap. The optimal market and customer strategy can help an organization turn customers into advocates, infuse customer interactions across each channel with positive impressions of the brand, and help engender a feeling of loyalty across the customer base that drives campaign performance and positive business results.
No matter what CX Solution you have make good business decisions with Big Data Analytics!
Bill Cook
Below is a 5 megabyte hard drive being shipped out in 1956!
