Will Retailers succeed with e-Commerce because of Data Quality?

Trends from the Nordics are confirmed in England. Retailers with physical stores are the fastest growing in the online shopping sector.

They have been a little slow to roll out, because of challenges like cannibalization, logistics and branding. I know that several chains are on the move to launch large worldwide shopping sites. In my opinion they will be highly successful and pass the early movers on the net. My basis for this is that they take the challenge of Data Quality seriously – maybe not by choice but out of pure necessity.

The retailers have multiple points of sales, and multiple point of customer data storage.  Point of sales can be Telephone/Customer Service, Customer Clubs, Physical Stores and customer data storage can be in CRM, ERP and logistics system.  In addition they will have several brands where also the customer data is stored in.  When they then try to do multichannel marketing, it is impossible with the structure they have today.

Multichannel seems a little challenging in this picture……

The solution the chains choose is to make a Master Database, with one customer ID – with link to each brand. When you have the Master Data, you can start analyzing for cross selling opportunities. If you are a customer of Brand one, you are also a likely customer of brand 5.  In addition you have tools to filter the information from the multiple points of sales and data. When customer data is entered in one of the point it is checked for duplicates, matched to the right record, checked for fraud. In addition you can set up error tolerant CRM for the point of sales and enrich the data with Reference data.

Now you are ready forMultichannel Marketing!

The first movers, who often were pure online players, have not had the need for such a rigid Data Quality setup. Where retailers now use professional tools, the pure players still trust their homemade. They will wake up one day and wonder what happened?

Voter fraud or just poor Data Quality?

What would we say if an African or South American government sent out election card that:

• Says you will have to cast your vote in a non existing election location
• Gives you the wrong address to the election location
• Tells you to vote at two different location, but you should only vote at onel
• Tells you to vote at two different locations, but you should actually vote at a third location?

With our prejudices, I think we would cry “voter fraud”. The above mentioned is not in Africa, but is happening in the Danish EU election. The newspapers bring new stories every day.

It is of course not voter fraud, but just plain poor Data Quality.  Is it much better that poor Data Quality influences the results of the democratic process? In Denmark the EU is already charged with having a democratic deficit, so this is the way to go!

The sad thing is that all this problems could be solved easily with the right process and tools.

You can read more about this in Danish on Politiken.dk

Focused vendors get highest customer feedback in new Data Quality Research

The Information Difference Ltd – a British Analyst Firm has made a research about the vendors on the Data Quality Market. You can read the full report here.

Here is the Diagram that Information Difference has made of the DQ Vendors.

The major vendors in the data quality market are described using the Landscape diagram that follows (see later for more on how this is derived; note that due to a modification in the methodology this time around—this version includes customer satisfaction feedback—like-for-like comparisons between this chart and previous versions should be treated with caution).

Another interesting conclusion in the report is that:

The highest customer feedback scores in this research were for Melissa Data, Datactics and Omikron.

These vendors are focused on solving the DQ challenges at hand. Sometimes so focused so the larger vendors call them Niche vendors. Maybe providing fast and correct result can be as important as delivering largs suites?

Is Data Quality finally getting traction?

I have just attended MDM Summit Europe 2009 – very interesting!

This was about Master Data Management – but single most theme that was discussed was Data Quality.

Statements like: “Data Quality is MDM”,” MDM has to have the best Data Quality” was repeated throughout the Summit.

Can we hope that the issue of Data Quality is finally getting the traction it needs?

We need more of these Data Quality Summits!

Checklist for search in CRM/ERP systems.

It is estimated that 76% of poor data is created in the data entry phase.  Mostly this is due to the fact that the operators simply cannot find the customer, supplier or product.  If you enable the operators to find the record, you will instantly get better data quality.

I have made a checklist of what efficient search for CRM/ERP should be able to handle. You can find the checklist here.

You can also read more about CRM and Data Quality here.

Quiz about Data Quality

Are you situated in Norway?  My company is running a quiz about Data Quality until the 15th of may, with a new question every week.  You can win a Washing Machine (since this is what data cleansing, data scrubbing is all about)  or a gift card from Elkjøp of 10.000 NOK.

We do this to put the focus on Data Quality, with capital DQ.

You can access the quiz here:

Increase your revenue by 66% with Data Quality

I stumbled on this interesting article in destinationCRM.com. It covers research from SeriousDecisions about how best practices in Data Quality can boost revenue by 66%.  Best practice in Data Quality has earlier been proven to be a key to success when you implement MDM solutions.

I have tried to show the cost of poor Data Quality, and it is good to show the benefit of optimal Data Quality.

There are several key areas where superior data management can have discrete benefits, according to the report. These follow the SiriusDecisions Demand Creation Waterfall methodology:

• From inquiry to marketing-qualified lead: It’s most cost-effective to manage data at this early stage, rather than let flawed information seep through the organization. A data strategy that solves conflicts at the source can lead to a 25 percent increase in converting inquiries to marketing-qualified leads.
• From marketing-qualified lead to sales-accepted lead: Bad source data is compounded by the use of multiple databases and formats, leading to distrust of marketing’s work by sales. Unifying the data, whether into one database or by using technology for virtual integration, can lead to a 12.5 percent uplift in conversion rates to the next stage.
• From sales-accepted lead to sales-qualified lead: Scoring becomes important at this stage, as the sales team goes to work on the leads it can use — and returns others to the marketing team for further nurturing. Clean data can reduce by 5 percent the time spent conducting the kind of additional research that precedes initial contact with a prospect.
• From sales-qualified lead to close: The benefits seen between sales qualification and close magnify those accumulated during the previous stages, as salespeople continually update the status and disposition of the potential customers. “Given that the average field-marketing function spends no more than 10 percent of its budget in support of this final conversion, accurate data is a must for applying the right tools and resources to the right audience at the right stage of the buying cycle,” Block writes. A single system of record to keep marketing and sales on the same page — cultivated by timely updates by all involved parties — is critical.

The impact of these abstract concepts — the true value of data management — becomes quite clear as soon as real numbers are applied: From a prospect database of 100,000 names, an organization utilizing best practices will have 90,000 usable records versus a typical company’s 75,000; at every stage thereafter, the strong company has a larger pool of prospects with a higher probability of closing. In the end, SiriusDecisions can show 66 percent more revenue for the company with high-quality data management.

This shows me that the Data Quality Firewall and the new concepts I introduced in September 2008, is the best way to optimize the data. The earlier you detect and correct poor data, the higher your revenue will be.

Lack of good data makes executives rely on gut feeling

In December Accenture published a survey called:  Most U.S. Companies Say Business Analytics Still Future Goal, Not Present Reality.

The interesting findings:

60 percent of major decisions are based on analytics and 40 percent are not.  Why do still 40 percent base their decision on gut feeling, rather than analytical skills?  61 percent answered it was because no good data was available.

Makes you think, doesn’t it?

“Standardizing and normalizing disparate customer data is akin to having root canal surgery at the dentist.”

I found another interesting article on SearchDataManagement.com about why Data Quality is elusive at most organizations.

Most interesting points according to Gartner:

• Half of the companies have actually deployed data quality tools or started Data Quality initiatives.
• Of those who use data quality tools, less than one third has deployed these enterprise-wide.

The reason is that the information is siloed in different databases collected from different sources, with no tools to connect them together.  Or as Leslie Ament, Managing Partner, Hypatia Research says:

“Many larger retailers have upwards of 10 different databases with different schema for collecting customer data,” Ament said. “Standardizing and normalizing this information is akin to having root canal surgery at the dentist.”

I like analogies like that, hopefully we can make customers address the challenges, even though it hurts.  The good thing is that I think we can see more business is becoming increasingly aware of these challenges.

Read the whole article here.

10 Critical questions to ask when you implement CRM

I came across an interesting article called: Saving CRM: Creating a data quality program by Douglas Ross.  Mr Ross is Douglas Ross, VP & CTO, Western & Southern Financial Group, so it’s from a users point of view, and not a vendor.  This is a well pointed article I urge everybody to read.

The most interesting part of the article is the list of 10 Questions you should ask before you start the project.  I have listed them here.

1. Have the benefits of improved data quality been defined for and agreed upon by the senior executives in the business?
2. Has the organization defined architectural standards for data, the relationships between data items, and requirements for data usage including those levied by the audit, regulatory, and compliance areas?
3. Does the organization measure data quality and strive for continuous improvement using agreed upon metrics, scorecards, and dashboards?
4. Are data-entry personnel equipped with tools to help enter clean data into the target systems?
5. Are there formal data stewardship roles, and are the related processes well-defined?
6. Do the systems you intend to integrate all support a universal, immutable customer identifier?
7. Do the target systems support all the necessary data elements and the actual relationships between products, people, accounts, and employees?
8. Do the target systems cooperate with one another to maintain data integrity, or do they “fight it out” and overwrite one another’s information from time to time?

9. Has the organization undertaken a bulk cleanup project to cleanse or rationalize legacy data?
10. Does the IT organization understand the benefits of clean data in driving improved business results?

If you answered “no” to nine or more questions, you’re in the same boat with a lot of other organizations.

I hope these questions will be mandatory for future implementations.

Read the article here.