Experienced marketers will tell you that the customer journey begin long before reaching the shopping cart. You can begin to win the prospects’ favor from their first curiosity about your product, throughout the nurturing process, until they reach the final stages of comparison shopping and conversion. To effectively channel these customers down the sales funnel, you want to remain in-tune with their state of readiness and be equipped to offer the right incentives at the right time.
But, some concerns arise during this process such as “What is the right incentive?” and “How do you determine the right timing?” These questions are further complicated by the fact that every customer is different. Figuring out the best conversion strategy for each individual requires just short of being able to read their minds, tens of thousands of minds, at a moment’s notice. How can anyone hope to attain this kind of marketing intelligence?
Fortunately, today’s playing field has been leveled: all businesses can and must embrace this capability now. With the emergence of Big Data technologies and recent advancement in analytics, offering a fine-grained customer experience across all channels is no longer just in the domain of giant multi-nationals. The cloud infrastructure removes the economic factor is utilizing unlimited compute and storage capacity. And boutique consulting firms like BigR.io deliver the quantitative expertise that makes analytics initiatives low-risk have-to-having propositions.
Modern commerce is predominantly conducted via digital channels. This allows suppliers to collect unprecedented amounts of data on their customers. Advanced analytics techniques turn this data into insights that help businesses serve individual customers better and win more sales.
A 360-degree customer view yields a complete profile by incorporating all available information, whether from internal repositories, email, voice records, or social networks. Such a detailed understanding translates into a superior customer experience, improved campaign effectiveness, boosted sales, and better retention.
Customer Journey – Track each customer’s digital trail and decipher sentiment relative to the experience. These interactions contain the clues on how best to engage prospects and when. Examples of the customer journey include:
Customer Acquisition – Customer profiles combined with nurturing collateral are the key to winning the hearts of new clients. The 360-degree view is the basis for tactical engagements through all touchpoints, from the first exposure to the final conversion and continued engagement.
Customer Churn – Conversely, every existing customer retained is worth a new customer earned. Existing clients who exhibit signs of dissatisfaction or inclinations towards switching can be detected through real-time analytics and remedial efforts can be made in time to retain loyalty.
The ultimate customer care is when the buyer is automatically given or presented with the most probably match to their needs or wants. Personal preferences are ingrained in the breadcrumbs left along the entire digital journey. BigR.io can help you collect, analyze, and serve them up for each and every instance of customer interaction. Implementation of personal profiles as active records means dealing with the challenges of rapid access and sheer volume; these are the very missions of Big Data engineering. By embracing the new generation of computing infrastructure, the forward-looking enterprise can incorporate this high degree of personalization into daily operations.
Deciphering social media sentiment is a fascinating and daunting challenge. All the factors that motivated the Big Data movement come together under one roof. The data is unformatted, sketchy, nearly impossible to authenticate and validate, and often short-lived in relevance, yet likely to be highly honest. Once the customer’s identity is mapped to the user ID in the CRM, the marketer gain insight about a person’s habits, range of motion, purchasing pattern, and strongly held opinions that otherwise would not be revealed in a targeted survey. As a whole, social media data also points out market trends ad helps companies adapt to changing customer tastes. This effort is one of the most important steps in completing the 360-degree view of your customers.
Customer Insights become gold if the business can plot the Next Best Action (NBA), or the appropriate course of action that matches the customer’s individual needs. Adaptive, data-driven decisions are far superior to gut feelings and intuition when it comes to consistent results. Predictive analysis finds the best combination of timing and marketing levers that elicit the optimal response from the buyer. Call center operators can work from a list of most-likely customer concerns before picking up. Visiting representatives can arrive at meetings prepared with the most appealing agenda. Customer support agents can proactively offer assistance or promotional material before frustration builds.
The model itself can be retrained at the appropriately set intervals to remain adaptive. Through Predictive Model Markup Language (PMML), personalized NBAs can integrate directly into operational systems such as marketing automation systems, call center software, consultation scheduling systems – anywhere automated or manual processes take place.
Today’s marketing activity has moved far beyond the back-office CRM. Marketing automation systems, ad targeting, email delivery systems, A/B and multivariate testing tools, consumer surveys, crowdsourcing, and social media monitoring are all necessary parts of a complete arsenal to achieve marketing success. The escalation in tools and data also means the enterprise must utilize cutting-edge analytics to orient its campaigns and drive personalized marketing efforts in an ever-more competitive landscape.
A recommendation engine is the most obvious eCommerce strategy for any business trying to boost revenue from its existing client base. Collaborative Filtering is the most commonly-recognized technique for finding likely customer-product matches given only sparse purchasing history data. As a recent innovation, Factorization Machines far exceed collaborative filtering in recommendation precision to the point where a single customer-product pair can be identified to se the recommendation score. Additionally, this new technique incorporates cross-feature influences which may be too important to be overlooked. For example, a viewer may actually dislike romance and comedy movies, but is fascinated by romantic comedies.
The sales funnel provides an important overview for directing sales efforts and organizing campaigns. While marketing automation platforms offer prospect scoring, the feature is always set manually. Advanced analytics techniques, such as the Hidden Markov Model, can automatically and optimally score each prospect based on digital behavior. Digitally-intelligent enterprises can leapfrog the competition with aggressive adoption of data-driven innovations and harness these important hidden indicators.
While fine-grained personalization is well within the capability of today’s app-serving platforms, from a management perspective it is of practical necessity to conduct campaigns at a cluster level. Analytically sophisticated organizations can make guided decisions in choosing amongst a host of clustering techniques from simple solutions (k-means) to advanced (latent classification) to handle data at the extreme scales of volume and complexity. The mathematically derived classifications reduce dependency on gut feelings in favor of consistent returns.
There are a number of use cases where a timely response from an organization is vital to success. A dissatisfied customer can exhibit familiar disapproving behavior patterns right before defecting to competitors. Other digital signatures are indicative of high receptivity to promotional efforts. This concept can be extended to fraud transactions wherein the urgency for response in these cases are self-evident. Streaming data analytics brings real-time pattern detection within reach of every progressive organization.
Effective marketing campaigns are essential to business expansion and bringing in sales. BigR.io can help you design and build your Data Management Platform (DMP) to help drive your marketing initiatives. A DMP incorporates data on retail transactions, catalogs, social media, and online advertising. Our DMPs support hypothesis-driven, high-impact analyses and help your organization move from simply collecting data to surfacing actionable insights and delivering business results.
Let’s use a fictional company called Ace Shopper (“Ace”) and a fictional customer named Hank to illustrate the above concepts in action. Ace is a specialty consumer electronic superstore that actively profiles its buyers with customer 360-degree view analytics. Their records have Hank on file as a busy executive with three teenage children, two of whom are musically gifted. In the past 3 years, Hank’s family has spent over $10K/year on musical instruments and accessories in the store.
Ace’s marketing department categorizes its customer population based on the most distinguishing features, which are found to be indicative of their spending patterns. Hank is categorized in a group containing highly-educated parents with college-bound children. His purchase records show a preference for high-end electronics and incidental interest in laptops.
When a new electronic keyboard came onto the market, Ace’s marketing department ran their recommendation engine and found Hank to be a top 10% match. Ace proactively includes Hank in their email campaign on seasonal product releases. The email addresses Hank by name and thanked him for his purchase items in the past on year.
Soon after the email campaign, the customer tracker application lists Hank as a customer whose interest level moved from “unaware” to “interested” due to his short visit to Ace’s eStore. The marketing platform does not fire additional emails yet, but starts retargeting Hank with ads for musical instruments.
In a few months, when summer break nears, the tracking application picks up frequent visits by Hank on the eStore and each time the click trail dives deeper into product details. Click-tracking software also picks up signs of comparison shopping. Hank’s readiness status is now upgraded to “active”. With this status, the marketing platform now authorizes promotions valued up to 5% of Hank’s annual purchase.
At the same time, one of Hank’s Facebook influencers posted a complaint about a failed circuit board on an electric guitar, to which Hank actively responded. Apparently, this issue worried Hank sufficiently enough that he opened a chat session with an Ace sales agent. Before responding, the agent sees Hank’s readiness status, purchase history, issue list, and suggested promotional offers prepared by the customer analytic engine. The agent greets Hanks, assures him on the quality of the new product line, and offers a free two-year extended warranty plus an invitation to attend an in-store live performance by a famed local artist. Within a week, Hank became a happy repeat customer.
The 360-degree customer view means a complete understanding of a client’s needs and a personalized customer care presence. This level of intelligence and responsiveness can only be achieved with a highly-integrated system that can readily access all data sources and continually refreshes the status of the customer.
Big Data technology and advanced analytics brings fine