One brand that’s carved out a successful program as an alternative to third-party data is The New York Times. What’s more, the tools that it’s building will support various parts of the company beyond the marketing department. A piece in AdMonsters takes a look at how the Times’ data strategy supports a view of the customer journey from end to end.

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The technology allows organizations to process large quantities of data and glean insights from it more quickly and on a self-serve basis. For Pandora, creating audience segments previously required the use of manual tickets and analysts to run queries themselves. Now, the company is able to automate marketing operations and engage its 60 million monthly active users using its trove of historical data. The result has allowed the platform to cut marketing acquisition costs in half and customize audiences more efficiently.

Additional clients of ActionIQ are The New York Times, Shopify and American Eagle Outfitters. For a deeper dive into Pandora’s data and martech strategy, read more in AdExchanger.

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Software has solved some of these issues of speed and relevancy by enabling marketers to automate campaigns and emails, text alerts and other online actions to thousands or millions of customers based upon user behavior, such as an abandoned shopping cart. Yet as the corporate software footprint has grown—multiple systems and data stores inside and outside the company—new issues have cropped up.

Take the example of a large airline which must book thousands of trips per minute while also communicating on schedule changes, flight delays and other critical alerts around the clock. The infrastructure to handle that transaction load is enormous. When marketers want to access that data to offer real-time promotions to customers, such as giving a discount to a customer who had a bad experience, they don’t get top billing from IT. The data from internal CRM, billing, operations, frequent flyer and customer service systems isn’t synched regularly with marketing systems.

That means marketers can’t act upon customer needs in the moment or even in a few hours. Marketers may get a portion of this customer data, but not enough or fast enough to take full advantage of real-time marketing opportunities—and that means lost revenue opportunities as well. Data from Evergage shows significant benefits from real-time marketing, including increased customer engagement (81%), improved customer experiences (73%), and increased conversion rates (59%).

The problem with marketing tech and real-time data

To be proactive and innovative as a marketer, one must have the ability to be spontaneous. Taking advantage of unpredictable opportunities to engage customers as they arise, related to sudden changes in the weather, news events or customer service events, for example, requires a highly integrated environment. Unfortunately, the tools that marketers use don’t often contain the latest data on customer activity.

SaaS marketing tools have contributed to this challenge of data synchronization. Working with vendors to update marketing databases in the cloud isn’t always quick, accurate or simple. What about the magic of APIs, you ask? Certainly, the advent of the API economy means that companies can exchange data between systems on different networks in different platforms with much greater ease than in the past.

Yet APIs are not a cure-all. APIs take some time for IT to code and implement, especially when IT is creating connections from many internal systems to the SaaS providers. Those APIs must be maintained and updated and are prone to errors, as networks are not always up on both the sender and receiver side. Over time, data invariably gets out of sync. As well, companies require ample internal network infrastructure to move these massive data feeds in a timely fashion. The more frequently the data changes, the harder it is to keep the data in sync.

Marketers have adjusted by cherry-picking data to be uploaded to their SaaS providers for specific campaigns, while leaving behind data that might not have an immediate purpose. This is fine for scheduled campaigns, but it’s not ideal for when a new or real-time marketing opportunity arises.

Bring back the data

Given the above challenges, companies are now reevaluating their past strategies of outsourcing marketing data and capabilities beyond the company walls. Bringing customer data and systems back inside the company gives marketers direct access to internal data sources. There is no lag or additional processes required to work with outside vendors, nor the need to replicate data. It’s easier to achieve a single data repository of customer data, which is important not only for marketing and sales needs but also for data privacy and security requirements. Best of all, marketers can do more of the real-time marketing work, which drives customer engagement and sales.

Companies can achieve this without abandoning cloud strategies, too. Instead of serving as the storage mechanism for customer data, the cloud can be used to manage complementary activities such as campaign management and tracking.

Creating an internal data repository for customer data will take time and investment for most companies. When asked how much customer data lives in a central data warehouse, 95% of respondents said that at least some of their data is located in a centralized database, but only 34% say most of it is, and just nine percent have everything in one location, according to “Email Marketing Trends & Best Practices,” a 2016 survey conducted by The Relevancy Group. Yet as customer intelligence and real-time marketing become increasingly important to customer loyalty and revenue growth, executives will find the prospect of having all customer data in-house hard to ignore.

Dan Roy is Co-founder and CEO of MessageGears.

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When we ask customers to share, tell us their shopping preferences and lifestyle activities, or show photos of their latest grocery haul, we must deliver relevant content. Otherwise, we risk of losing trust and interest.

The problem is that study after study has shown that consumers think brands don’t understand them as individuals. Even more troubling is the fact that despite new data-capture capabilities, many brands admit they are ill-equipped to customize conversations with consumers.

When brands ask customers to share personal information, they’re entering into a sacred pact, with the expectation that customers (and their insights)) will be treated with respect. They expect personalized communications in exchange for that data. This pact is so important because the customers who share are the most valuable. The sharing gives brands the potential to build a rich, fruitful, two-way and create influential advocates.

But there are potential pitfalls. The first instinct when brands gather customer information is to try to sell them something. That’s wrong. Take a look at something like the Nike+ community. The entire premise of the community is around leveraging data. They know that to serve their customers, they must focus their community around the quantified self.

One study found that 80% of companies don’t know their customers beyond basic demographics and purchase history. It’s imperative to dig past purchase behavior and understand a customer’s lifestyle as well as their mindset along the path to purchase. Don’t forget to create a feedback loop. Let consumers know that because you know XYZ about them, they are receiving ABC offer. This helps.

When one of our clients wanted to promote their philanthropic initiatives, they knew the most powerful consumer advocates were going to be aligned with the underlying mission. To find those consumers, the brand leveraged key survey data and demographic data from a community of advocates. By identifying not only advocates who align well demographically, but also those with an emotional connection, they were able to supercharge the word-of-mouth surrounding their charitable causes.

Aligning your CRM with other customer-data centers is daunting. To become a brand your customers trust, the first step is to fix what’s broken internally, and then to develop content, offers and promotions that your customers perceive as helpful and relevant to them and only them. Then, you’ve got a real conversation going.

Susan Frech is CEO of Social Media Link.

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One big issue is timing. More specifically, it’s the ability to analyze individual customer actions as they occur over time. New research shows that if you can identify granular information about customer actions, in sequential order, you can more than double the number of churners you identify and with enough time to intervene and potentially stop them from leaving. The research is based on the results of a head-to-head comparison with a traditional, non-sequential churn model.

Today most marketers aren’t able to do this. Typical customer churn models deliver information that’s too late or too difficult for discovering insights. For example, consider your regular monthly marketing report that indicates 15% of customers are likely churners. You might be able to dive slightly deeper, and note that 80% of those churners made three or more customer care calls the month before they left, but the timing of customer activity is flattened into aggregate numbers. You can dig into the data to then try and intervene, but it’s a manual, time-intensive data-wrangling task. Even worse, the model that identified those likely churners may depend on that aggregated data because it can’t take into account the sequence of events that occurred.

A head-to-head comparison: non-sequential vs. sequential

A recent study by Amplero compared a brand’s traditional churn analysis model against a new sequential model, and found that the major missing factor was timing. Traditional churn models typically identify the red-flag behaviors that lead to customer defection, but they don’t consider the sequence in which those actions occur. As a result, marketers are limited in their ability to identify customers who will leave.

With the goal of determining whether sequence had an effect on identifying churners, separate data scientist teams built two distinct models to analyze the same, real-time data of roughly 1.7 million customers. One model aimed to identify churners in the traditional way, and the other was developed to recognize predictive churn behaviors in sequence.

For two months, the teams analyzed the data to predict which customers would eventually leave. What they found was that the sequential model demonstrated a dramatically more accurate way to identify churners.

In the study the new sequential model correctly identified churners at a much higher rate than the traditional model.

Specifically, what the numbers show is that for long-tenure customers (those who had been with the company for at least 6 months), the traditional, non-sequential model can identify 36% of customers who eventually left. That’s not bad – it’s more than double what you would get if you tried to identify churners at random. However, when the data scientists used the sequential model, they identified 75% of the long-term customers who would eventually leave. That’s more than twice the number you could capture with the traditional model. And obviously, the more churners you can identify, the better chance you have of taking action with those customers to reduce your overall churn rate.

What the research also revealed was the effect of the new sequential model on short-tenure customers (those who had been with the company less than six months, and down to just two weeks). The traditional model couldn’t identify a single short-term customer who was in danger of leaving, because it was only able to analyze and report data on a monthly basis and required six months of history. The sequential model, however, identified 79% of short term-customers who were in danger of leaving, based on as little as two weeks of data.

Why is sequence so important?

To discover why timing and sequence made such a big difference in identifying churners, the team closely examined one common indicator of churn: calls to the customer care center. They found that a customer care call followed by at least a week of a customer’s continuing use of the service lowered that customer’s risk of leaving by about 10%. Even though a call to customer service usually means something is wrong, the timing and sequence (the call, followed by the customer’s continued use of the service) actually indicates a healthy customer relationship – the customer called about a problem that got resolved.

Using this one example, you can see why specific behaviors and sequence matter when predicting churn. A customer care call – or several – doesn’t tell you much about a customer’s attitude toward a product or company. That call needs to be considered within the context and timing of the customer’s other behaviors.

Integrating sequence into overall marketing efforts

When you add time and sequence to the churn modeling mix, you get much better results. The benefit of a sequential churn modeling technique vs. industry standard modeling techniques is that it acts on the time series of a customer’s behavior and automatically discovers which signals are meaningful. With machine-learning technology, marketers can analyze when a customer takes a specific action, in addition to what actions that person takes. Importantly, you can do this over time identifying subtle behavioral patterns that otherwise may go undetected.

Imagine the possibilities if you could predict which customers are going to leave, and identify those customers more quickly than ever before. You would have the time to follow up and resolve customer issues, or present products and offers that addressed the specific needs of those customers. Identifying churners early and often gives you the opportunity to strengthen customer relationships so that customers see greater value in your product or service and stay with you longer.

Dr. Olly Downs is the chief scientist behind Amplero, a self-learning personalization platform built by Globys.

More on Customer Data & Retention:

Special Report: CRM Tech Trends

20 Top B2B Customer Engagement Tips

Why Customer Segmentation Isn’t As Important As You Think

Leveraging Customer Data Without Over Investing In New Technology

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