Marketing
In a world of choice what opens wallets? What moves share? What fosters consumer loyalty?
Opera's Marketing Solutions has the answers.
From universally available products to loyalty and incentive programs galore, it's harder than ever to be heard above the noise and to compete successfully and profitably. Customers have more places to shop, an abundance of margin-squeezing offers to accept, and fewer reasons to be loyal to any one marketer. But Opera can use advanced analytics and ongoing decision guidance to create the type of customer knowledge, insights, and offers that make a profitable difference. With Opera's capabilities and your Big Data reserves, here's what you can do (click on each to learn more):
- Turn raw data flow into a strategic weapon with a Marketing Signals Hub
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As a marketer, you may have more information than ever before. But the complexity and volume of the data flow — now coming in massive volumes from mobile phone location services, web browsing, and more — make it harder and harder to use it to make a difference. Opera's Enterprise Solutions services can create a custom Marketing Signals Hub that brings together your data flow in a single, dynamic, integrated place.
If you're trying to do this yourself within your existing infrastructure, you are probably facing high IT investment and more than a year's build time. But Opera can deliver this in months, not years — and for minimal IT expenditure, not many millions.
Through our Vektor™ Big Data analytics and processing platform, we provide the infrastructure, as well as ETL and data management capabilities. We can rapidly connect to multiple data sources (internal and external) regardless of platform; normalize, cleanse, and integrate ongoing data flows; and leverage Vektor's highly flexible processing infrastructure to select the right tools and technology to meet your performance, security, and effectiveness needs. Then, we apply powerful analytics against this stream of organized data to deliver the key insights you want, in the form you want them, to any interface you desire. And we set up and manage ongoing production.
- Understand the patterns of the entirety of a customer relationship over time — and change its course
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Finally, there's a new and powerful way for marketers with closed loop systems (e.g., direct and online marketers and those with loyalty card programs) to fully leverage their unique data resources: Touch Curricula. We start by using advanced temporal pattern recognition and complex clustering techniques to get an in-depth view of the flow and patterns of each customer's relationship over time. We weave together all touch points (customer service calls and outcomes, web visits, promotional activities, purchases, and more) plus a wealth of relevant external information to extract the behavioral purchase patterns that tell us where, when, and at what price we can trigger a customer to buy.
Unlike static snapshots, our techniques allow us to see otherwise imperceptible shifts to and from brands, categories, and products. Our look-alike models and other clustering algorithms help us find customers who should be buying in a category but aren't, or who are likely to be receptive to a particular type of promotion.
We use all this information to create a specific aspiration plan for each customer. Then, we serve up a series of offers to bring each customer closer to this aspirational goal. Over time, our carefully calibrated promotions and communications profitably change each consumer's behavior.
- Go far beyond the typical, "you liked that, so you'll like this" recommendations to create individuated suggestions that spur purchase and loyalty
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In today's complex world, with its plethora of choices, marketers need a great recommender engine to serve up powerful offers that go beyond a simple, "If you liked THAT, you'll like THIS." Instead, we tie our offers to each customer's patterns and behaviors. So the one-on-one conversations we have with customers include questions like,"You haven't bought this in a while. Would you like one today?", "We're glad you like this. Would you like another?", and "Did you know other folks like you tried this and really enjoyed it?"
With 160 machine learning scientists, Opera has world-class expertise in building the next-generation recommender engines that make suggestions like these possible. In fact, Opera's team was part of the consortium that beat 41,000 other teams to come in "first equals" (based on model performance) in the prestigious Netflix Prize competition to improve the movie rental company's recommender model. Our approach more than doubled the effectiveness of Netflix's existing system.
A great recommendation engine is critical, but so is implementing it into your operations. Through our Vektor™ platform, Opera has the ongoing delivery capabilities to do so — and without significant IT and infrastructure investment on your part. We can use our universal, systems-agnostic connectors to tap into your data flow to structure, process and apply our analytics against it, then serve up an ongoing stream of recommendations for each customer.
- Find faint signals before they turn into not-so-faint attrition
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Once they've left you, you face an uphill battle to get customers back. So Opera stops attrition before it starts with Attrition Reduction solutions, using advanced analytics to bring faders back into the fold. Opera's sophisticated longitudinal monitoring and temporal pattern detection focuses on understanding the norms and flows of each customer's relationship with you over time, so fading stands out more clearly. And we integrate a far wider range of signal-rich data in our models, including unstructured information. We not only identify faders — we offer specific action steps, too. For instance, we provide relationship managers a prioritized list of customers based on economic value and likelihood to defect, along with specific drivers of dissatisfaction and defined steps to reverse trends.
- Use powerful local economic indicators to segment customers and outlets in better ways
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Often, two retail outlets of the same chain just a couple of miles apart get radically different results from identical promotions. It's not just a quirk of nature — it's because geographic proximity is only one aspect in segmenting restaurants and retail stores into trading and promotional groups. Opera uses geospatial algorithms, along with local macroeconomic trends, imputed household socioeconomic information, and our proprietary Zip + 4 database to give a more nuanced picture of what drives results, and what doesn't. This information allows us to go beyond mere location proximity to segment customers AND retail outlets in new and powerful ways.
Any industry with a product or service to sell can benefit from Opera's Marketing Solutions. And many of them already are. To see how, click on the case studies below.
