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Customer Interactions

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By David Loshin

In my last set of posts, I suggested that organizations reconsider the scope of the concept of the "customer" and how redefining the relationship between the organization and a customer. More to the point, I wanted to begin to explore how managing the different aspects of the customer relationship can enhance customer centricity, improve the customer experience, and eventually lead to increased profitability.

I summarized with the suggestion that you consider every interaction with any entity (individual or organization) in which there is an exchange of value as a customer interaction, and that is the topic of this week's post.

That suggestion hinges upon two core concepts. The first is that one can effectively identify the scenarios within any business process where two entities interact and there is an identifiable exchange of value. The second is that one can describe and quantify what that exchange of value is.

We can begin with an assessment of the business functions that traditionally are associated with customer interactions and their processes. For example, the marketing function seeks to attract and engage prospects, while the sales function looks to convert prospects into committed purchasers.

There may be a fulfillment function tasked with delivery of the purchased product or service, the finance function to collect payments, and a customer service function to deal with inquiries and complaints. Each of these business functions has some interaction with customers; the challenge is to identify (and document, if necessary) the business processes and then specify where in the process the customer interaction occurs.

Those customer interactions will be the focal point of our next series of postings. Next we will consider the exchange of value, which frames the point of the interaction, and then we will look at the information about the customer that can be used to increase the value of the interaction.


Managing Customer Connectivity

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By David Loshin

At the end of our last entry, we had come to the conclusion that standardization of potentially variant data values was a key activator for evaluating record similarity when looking to group customer records together based on any set of characteristic attributes. From an operational standpoint, this activity is supported using data quality tools that can parse and standardize data.

But the process must go beyond the purchase and use of the tools. For any customer centricity program in which connectivity is relevant, there are going to be multiple dimensions of connectivity employed in business decisions. We can immediately fall back on my original example of the "household" grouping, and depending on the objectives for customer outreach and experience, other groups will be overlaid with each other.

Here is a clear example that builds on my post from a few weeks back. We originally suggested that the household was relevant for mobile telephone companies looking to expand residential customer commitment though increased product sales and service contracts within the household, since one decision-maker might be responsible for adding new lines and services.

That same mobile telephone company might also look at their business-to-business relationships and look to expand their footprint among business customers, suggesting a new grouping of customers based on their employer.

Overlaying the households and the corporate customers would provide a picture of companies existing brand predispositions among the employees. Identifying the key corporate decision makers and offering combined business and residential account discounts might be a good way to exploit knowledge of overlapping connected groups.

The result is that the analysis not only depends on good quality data, it assumes that good processes are in place for managing the hierarchy data that maps individuals into groups - an example of what could be called metadata quality. Keeping hierarchies of concepts, data attributes, and mappings among individuals based on those hierarchical attributes (and of course, similarity scoring for linkage!) is a valuable skill, one that we will revisit in upcoming series of posts...

Centricity and Connections: Clearing the Air

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By David Loshin

There are opportunities for adjusting your strategy for customer centricity based on understanding the grouping relationships that bind individuals together (either tightly or loosely). And in the last post, we looked at some examples in which linking customer records into groups was straightforward when the values to be compared and weighted for similarity are exact matches. When the values are not exact, it introduces some level of doubt into the decision process for including a record into a group.

Let's revisit our example from my last post by adding in a new record for evaluation:

John Hansen, 1824 Polk Ave., Memphis TN 38177
Emily S. Hansen, 1824 Polk Ave., Memphis, TN 38177
Emily Stoddard, 1824 Polk Avenue, Memphis, TN
We had already decided that John and Emily shared a household, but all of a sudden we have a third record with a name that shares some similarity, with one of the existing names, and an almost exact street address match (note that the third record is missing a ZIP code).

We could speculate that "Emily Stoddard" changed her name after she got married to "John Hansen," or that she changed an address somewhere as she moved form her bachelorette pad to their newlywed home. But without exact knowledge of the facts, it is only speculation, and one must exercise some care when relying on speculation for business decisions.

If a few small differences pose a challenge to linkage, what would you think of dozens, or even hundreds of variations for names, locations, or other data values?

Just as a case in point: in a hallway conversation at the recent Data Governance Conference, a colleague mentioned that one of his customers' databases had over one hundred variations for a certain big-box retailer's name! The conclusion you can draw from this is that a key part of the record linkage process involves some traditional data quality tactics, namely appending a standardized version of the data to help your linkage algorithms score record similarity as a prelude to establishing connectivity.

Customer Centricity and Connections: Establishing the Link

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By David Loshin

In my last post, we began to look at the value proposition for grouping individual customers into logical groupings. We began by looking at a grouping that generally appears naturally, namely the traditional residential household.

We talked about householding in a previous blog posting, but it is worth reviewing the basic approaches used for determining that a group of individuals share a household. The general approach is to analyze a collection of data records and examine sets of identifying attributes for degrees of similarity in naming and residence locations. Many situations are relatively straightforward, such as this example:

John Hansen, 1824 Polk Ave., Memphis TN 38177
Emily S. Hansen, 1824 Polk Ave., Memphis, TN 38177

In this example, two individuals share both a last name and a location address, and although the data evidence does not guarantee truth of the inference, it might be reasonable to suggest that because there is a link between the family name and the residence location, these two individuals are members of the same household. The algorithm, then, is to link records into a collection of similar records based on similarity of the surname and residence characteristics.

However, the concept of grouping is not limited to conventional groups, since there are many artificial groups formed as a result of shared interests or similarities in profile criteria. For example, people interested in certain sports car models often organize "fan clubs," new mothers often organize toddler play groups, and sports team fans are often rabid about their franchise alliances.

In turn, your company might want to create marketing campaigns that target sets of individuals grouped together by demographic or psychographic attributes. In these cases, you would adjust your algorithms to link records based on similarity of the values in other sets of data attributes.

Establishing the link goes beyond looking at the data that already exists in your data set. Rather, you may need to append additional data acquired from alternate sources.

And, interestingly enough, you will need to connect the acquired data to your existing data, and that requires yet another record linkage effort. Apparently, understanding customer collectives is pretty dependent on record linkage. And while linking records is straightforward when all the data values line up nicely, as you might suspect, there are some curious intricacies of linkage in the presence of data with questionable quality.


Customer Centricity: Establishing Connections

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By David Loshin

In our last set of posts, we looked at the relationship of location to a strategy for customer centricity, and one conclusion we can draw from that discussion is the value of organization into groups for better understanding what drives customer behavior. To say it a little differently, if "birds that flock together" are "of a feather," it suggests that "feather-similarity" defines a set of one or perhaps more relationships that are of interest to your organization.

This is probably not a concept that is unfamiliar within your organization. Many companies have diligently attempted for many years to track at least one specific group relationship: the household. Simply put, a household is a concept representing the collection of individuals that share a residence, along with all of the attributes and characteristics.

The concept of household is particularly relevant in business scenarios in which products or services can be up-sold or cross-sold by broadening the delivery to a collection of individuals based on the choice of a single group decision-maker. Here are two examples you are probably familiar with:

• Mobile communications - Once a household decision-maker has opted to contract with a mobile telecommunications provider, that provider looks to cement the customer commitment by leveraging promotions for purchasing additional devices and services for others members of the household.

• Retail businesses - A retail company will want to know the make up of the household to better direct market items targeted at filling the needs of the different individuals for whom the head of household makes purchasing decisions.

In general, understanding the makeup of a household helps in devising marketing strategies based on the characteristics of the group rather than of individuals. For example "household income" might be a better indicator of a family's wealth than looking at the annual income of either the husband or the wife.

And the notion of a household can be abstracted in reference to other sorts of collections of individual customers, and in the set of posts, we will look more at the customer centricity concepts associated with connectivity and grouping.


Melding Aspects of Real-Life and Virtual Contact and Location

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By David Loshin

In the most recent posts, we have been exploring the emerging opportunity for developing demographic profiles for customers based on their virtual locations. More to the point, if we are using real-life (yet two-dimensional) geographies to help in developing customer profiling and segmentation models, how much more interesting would those profiles be when expanded to include behavior characteristics associated with many more dimensions?

In real life, (as my father used to say), you can only occupy a single chair with a single bottom. But there are many virtual spaces that can be occupied by one individual simultaneously, providing multiple dimensions for behavior analysis. I can have a presence on any number of social networks, play different online games, post comments at different venues, and tweet about all of these, almost all at the same time.

Not only that, but recall that all transactions take place with the parties in a real location, and that goes for online activity - much of our actions are still pegged to some documented location, usually by IP address, which can be resolved geographically based on the Internet topology maps. We can link real-world individuals existing in real-live space to online activities, and we can link online activities to real-world locations and real-world people.

By melding the characteristics of individuals associated with the different virtual spaces with those characteristics associated with physical contact mechanisms and locations, you begin to develop different segmentation models that can intersect with real-world locations in different ways. Perhaps improved resolution, precision, and hopefully quality of these expanded models will account for any diminished precision associated with the gradual anachronistic features of traditional approaches to geographic localization.


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