4 factors to make direct marketing campaign processes more efficient

Transparency, consistency and speed

In this video, Corios is pleased to share a walk-through of a refined and more efficient marketing campaign process for a marketing team who is currently struggling with large number of campaigns, a high degree of decentralization, and a need to make all their campaign processes more agile and nimble.
By refining their processes, they hope to create greater consistency in their campaign processes, to reduce complexity and technical risk, to automate their campaign quality assurance processes, and to create greater transparency through change management.

4 things CRM systems don’t deliver for lead management, but should

There isn't a magic silver AI or machine learning algorithm either

There are four essential capabilities that relationship managers need to properly manage their leads and client relationships, which CRM platforms really ought to offer out of the box, but which we’ve found are challenging to deliver in the real world.
These include:
  1. Client recognition, matching and householding
  2. Access to the complete view of the customer: including their products, transactions, and behavior
  3. Next best offer analytics
  4. Omnichannel marketing and sales orchestration
There’s no magic solution in prepackaged AI, machine learning and neural nets. Instead, this is where human subject matter experts, augmented by experience, insight and good data and analytics, are the key to building a sustainable solution.

My First Predictive Customer Journey Analysis: 2005

In the analytics media right now, “customer journeys” are cool and topical, but predictive customer journeys are even more inspiring, because they go well beyond telling you what’s already happened, into the zone of what’s likely for each of your customers to do next, and what you can do about it to present a timely strategy or treatment. This helps you improve offer timing, relevance and profitability.

You might have read some of our case studies in this area such as Five Lessons Learned From Predictive Customer Analytics or What Does a Predictive Customer Journey Look Like.

What you probably don’t know is, while customer journeys just became cool in 2017, we were building predictive customer journey strategies back in 2005 with a customer loyalty decision sciences program for one of the country’s prominent payment platforms.

Here’s how the program worked. The payments platform marketers selected a nationwide merchant for a sales stimulation offer, like $25 cash back at the point of purchase on a purchase of $100 or more. The strategy was that these could only be cardholders that hadn’t purchased anything using that card at that merchant’s stores anywhere in the country in the last six months. This dramatically increased the direct attribution of these offers towards cardholder response.

In terms of targeting, it would have been easy to simply contact those customers who had purchased at the merchant’s direct competitors, or had high RFM (recency, frequency, monetary) behavior in that merchant’s broader category (e.g., retail apparel, or airline travel, or small office/home office, or retail technology). Of course, we did use that sort of information as an input in our scoring and segmentation approach.

Looking back, we did something else that turned out to be even more powerful. We had access to 13 months of individual transactions for over 60 million cardholders. We built behavioral profiles of customers that ought to have purchased with that merchant (but didn’t), based on their purchasing similarity to other customers that actually had purchased from that targeted merchant. This meant looking at purchasing patterns across virtually every merchant category, the timing of those purchases, and the sequencing of relevant purchasing events by week and by month.

Using those advanced approaches, and comparing to several comparison approaches (i.e., using only purchasing history with the merchant’s direct competitors, or using random selection with the same cash-back incentive), we found our approach usually beat every other approach by a wide margin. Performance analytics revealed that most importantly, we could get the offer timing right based on who was primed for such a purchase.

We repeated this set of market tests for over 18 months, building more than a dozen of these major campaigns, improving the approaches, and generating tens of millions of dollars in increased spend incremental to the other test cells in our campaigns. We also leveraged this approach with some other credit card issuer clients of ours, seeing whether these approaches would work as well for customer retention tests as it did for sales stimulation (it did).

This is one of the client experiences that led us to create the Corios tagline, “We tell the story numbers can’t”, because we found we were literally telling a story about consumer purchasing patterns that perhaps nobody else had discovered yet. We were lucky to have access to such a massive repository of transaction data was so difficult to put in one place, given the technology capabilities of the time.

Since then, we’ve generalized the approach to go well beyond credit cards, and applied it in all sorts of other financial services, manufacturing and retail domains, for marketing, risk and financial crimes behaviors, and focusing on telling stories about the customer journey across the omni-channel experience.

Do you want to know more about predictive customer journeys? Send us a note to president@coriosgroup.com to start your own journey.

Humanizing Next Best Offers to Maximize their Effectiveness as Field Sales Leads

Next best offers: As marketers we have many offers we can target and deliver, each to a large pool of customers. The question remains, what are the best offers to make, not only measured by what’s best for your company, but more importantly what’s best for your customers. To qualify as “best”, these offers should be customer-relevant at that time in your customer’s relationship, and address their unmet needs.

The most conventional (but least effective) approach to select the best offers relies on sorting by the most profitable products and executing those offers to the company’s best customers. A more effective approach is to sort and target the best customers first; this is better but far from the ideal strategy. We’ve found the ideal strategy in almost all situations is to target individual offers to individual customers through the customer’s preferred channel at the best time for that customer. Sure, this is analytically and operationally challenging, but the results we’ve seen in more than 80 cases with our clients has proven this to be the winning strategy. We’ve also found that the technical and operational challenges are really not the limiting factor. Instead, it’s the change management required inside the company to fundamentally alter the way offers and channels are prioritized, how success is measured, and to convert the organization from a product-centric mentality to a customer and contact strategy focus.

In order to overcome this cultural challenge, we’ve found it is the most fruitful to put this capability right in the hands of relationship managers in the sales field, because their top-line revenue contribution is vitally important and they are always willing to consider prudent ways to spend more time with the customers who are actually interested and in the market. Correspondingly, we converted our approach to next best offers to not focus only on the needs of marketers, but also to focus on sales management and relationship managers.

How did we do this? First, we delivered our next best offer strategies through CRM platforms like Salesforce and Dynamics, which are the most common platforms our clients use.

Second, we converted the format of each next best offer into the structure that relationship managers want to consume. Specifically we added a sales narrative to each next best offer. Sure, we still perform our selection of offers using sophisticated analytics approaches, but frankly, the details of those approaches aren’t that relevant to our relationship manager audience. Instead, what they find useful are business-friendly English descriptions about each customer’s profile and what makes them likely to prefer that offer. This aids the relationship manager by giving them a clear set of tips to start the conversation. For example, “how other customers like you use this product, and the value they get out of it” is better than having only information from the product manager’s perspective.

We’ve also found that customers who are ranked as highly preferable to want a certain offer, score high on that offer for very different reasons. So we call out in simple business English the top 3 reasons that makes that customer a high scorer. This means the offers are not just marketing-qualified, but we can also speed up how quickly they become sales-qualified. This works for consumer offers and for business offers. Check out this example.

If you want to get to work on building this strategy for your firm, contact us directly at president@coriosgroup.com.

Predictive IOT to reduce mechanical failures and warranty claims

In pursuit of 99% vehicle fleet uptime

Corios helped a vehicle manufacturing client apply predictive algorithms to connected vehicle and service center geofence data to help them pursue fleet uptime of 99%, and to reduce mechanical failures and warranty claims. This was a fascinating application of predictive analytics to the internet of things (IOT).

Our client is a major truck manufacturer who has the strategic global objective of increasing vehicle uptime from 96% to 99%. How they intend to get there is to predictively identify vehicles at risk of mechanical problems before they happen, by combining real time data from telematics alerts, service center geofencing, repair histories and travel and road conditions.


Corios brought in a top-down view of the problem; we gathered data from a variety of sources, including vehicle telematics alerts, warranty claims, vehicle repairs, vehicle registration, vehicle service histories, and equipment supplier profiles. This became a massive collection of data, and we rationalized all this information so we could see a complete 360 degree view of every vehicle and its detailed history over time. Then we applied predictive analytics to rapidly identify the leading indicators of the equipment failures which led to the increase in warranty claims.