Data is the rich foundation for customer intelligence. So much can be learned and applied to speed acquisition of the right customers, grow them to full potential, and retain valuable existing customers.
All too often though, companies spend their time exactly where they shouldn’t. Rather than applying results from reporting and analysis to build customers relationships, they wash & wax their data. They just end up with something shiny.
That comes at a cost, as companies slow or forego the opportunities that could be realized if they focused on creating intelligence, not serving data.
Glorified Copy-and-Paste
Data may not be presented in a workable format to begin measurement and analysis immediately. If data is provided by different sources (divisions or channels, for example), it first has to be combined. Typically, that involves a glorified copy-and-paste exercise.
And then that data has to be standardized. Identifying the field names (“first_contact_date” vs. “con_dt_1”, for example), which will be combined, can be a complex step if team members don’t understand the data content. The values of those fields must be made uniform (“1-100” vs. “1-99”, for example).
This effort is typically highly resource-intensive, whether completed in a spreadsheet, data mart, or data lake.
These steps also can introduce errors. One incorrect entry can impact validity, sending teams chasing down ghosts, and/or driving flawed business decisions from inaccurate or incomplete source data.
The Potentially Hefty Price
The cost of any flawed decisions must be added to the resource and opportunity costs: time is better spent everywhere else—creating insights, conceiving resulting programs, serving customers or clients, business development, or leaving work at a reasonable hour.
Hampstead’s customer-focused solutions include an assessment of how clients support customers at key milestones. We often uncover gaps, redundancies, and waste.
Because of this perspective, when I find myself witnessing unproductive efforts, I calculate in my head the budget wasted. If the unproductivity I’m witnessing is a one-off, I assign hourly rates. If this waste occurs routinely, then I assume salaries and multiply the time to project the full-scale loss.
Then, I consider all the missed opportunities for what is typically expensive wasted time. The is such a thing as opportunity cost.
This exercise is not intended to be harsh.
What I’ve learned over years of consulting is that any process, however efficient or not in the beginning, becomes the standard over time. People fall into a “we’ve always done it that way” lull. They can’t solve, or they don’t consider there may be more efficient, tech-enabled, digitized, cost-effective, sanity-saving methods available.
That is typically the case when companies are stuck in inefficient data management practices.
The larger point is that time committed to moving and manipulating data is the tail wagging the dog. Companies may not realize that they have an alternative.
Happily, they do.
Look! No Hands!
Data manipulation processes can often be automated from end to end.
Beginning with data retrieval, data found in various sources can be collected at a predefined time and location. Whether from external sources (social media, partners, etc.) or internal (website, etc.), automating the collection process saves team members from going to one source, then the next, and the next.
That data can then be automatically normalized according to programmed business rules. This decreases the error risk, and all the resulting incorrect information and decisions that could follow.
Even standard analysis and key performance indicator (KPI) generation can be automated and presented to consumers of those results. Instead of massaging data to generate reports and analysis, those routine programs can be automated, too.
This automation doesn’t necessarily require the newest, shiniest technology. There are well-established tools and techniques that can get the job done. Once developed, they require limited investment and no specialized training for the team that consumes the output.
Full disclosure: when a data scientist is beginning a custom analysis, understanding the raw data is part of their analytic process. It should be. However, for the reporting and analytic work that many companies require, that is expensive, time-consuming overkill.
Higher, Better Resource Assignment
Instead of hours, and perhaps days and weeks and months committed to managing data, the process can be fully automated.
In fact, the process of managing data should be touchless and frictionless.
It frees resources for more important activities, including customer intelligence, critical approaches, creative problem-solving, customer storytelling.
That generates a far more valuable outcome than the mind-numbing practice of manually moving data from point A to point B.
Would you like to discuss customer acquisition, growth, or retention challenges? Or how to make data work for you? Let’s talk! Set up a 30-minute phone conversation with Marina.
Photo credit: Karen Maes.