The 12 Days of Shopping

According to IBM’s real-time reporting unit, Black Friday online sales were up close to 20% this year over the same period in 2012.  As for Cyber Monday, sales increased 30.3% in 2012 compared to the previous year and is expected to grow another 15% in 2013. Mobile transactions are at an all time high and combined with in store sales, The National Retail Federation expects retail sales to pass the $600 billion mark during the last two months of the year alone. While that might sound like music to a retailer’s ears, as the holiday shopping season goes into full swing on this Cyber Monday, the pressure to handle the astronomical influx of data collected at dozens of possible transaction points is mounting. From websites and storefronts to kiosks and catalogues, every scarf or video game purchased this season brings with it a variety of data points that must be appropriately stored, linked, referenced and hopefully leveraged. Add to that a blinding amount of big data now being collected (such as social media activity or mobile tracking), and it all amounts to a holiday nightmare for the IT and data analysis teams. So how much data are we talking and how does it actually manifest itself? In the spirit of keeping things light, we offer you, The 12 Days of Shopping…

On the first day of shopping my data gave to me,
1 million duplicate names.

On the second day of shopping my data gave to me,
2 million transactions, and
1 million duplicate names.

On the third day of shopping my data gave to me,
30,000 credit apps,
2 million transactions, and
1 million duplicate names.

On the fourth day of shopping my data gave to me,
40 returned shipments,
30,000 credit apps,
2 million transactions, and
1 million duplicate names.

On the fifth day of shopping my data gave to me,
5 new marketing lists,
40 returned shipments,
30,000 credit apps,
2 million transactions, and
1 million duplicate names.

On the sixth day of shopping my data gave to me,
6,000 bad addresses,
5 new marketing lists,
40 returned shipments,
30,000 credit apps,
2 million transactions, and
1 million duplicate names.

On the seventh day of shopping my data gave to me,
7,000 refunds,
6,000 bad addresses,
5 new marketing lists,
40 returned shipments,
30,000 credit apps,
2 million transactions, and
1 million duplicate names.

On the eighth day of shopping my data gave to me,
8,000 new logins,
7,000 refunds,
6,000 bad addresses,
5 new marketing lists,
40 returned shipments,
30,000 credit apps,
2 million transactions, and
1 million duplicate names.

On the ninth day of shopping my data gave to me,
90,000 emails,
8,000 new logins,
7,000 refunds,
6,000 bad addresses,
5 new marketing lists,
40 returned shipments,
30,000 credit apps,
2 million transactions, and
1 million duplicate names.

On the tenth day of shopping my data gave to me,
10,000 tweets,
90,000 emails,
8,000 new logins,
7,000 refunds,
6,000 bad addresses,
5 new marketing lists,
40 returned shipments,
30,000 credit apps,
2 million transactions, and
1 million duplicate names.

On the eleventh day of shopping my data gave to me,
11 new campaigns,
10,000 tweets,
90,000 emails,
8,000 new logins,
7,000 refunds,
6,000 bad addresses,
5 new marketing lists,
40 returned shipments,
30,000 credit apps,
2 million transactions, and
1 million duplicate names.

On the twelfth day of shopping my data gave to me,
12 fraud alerts,
11 new campaigns,
10,000 tweets,
90,000 emails,
8,000 new logins,
7,000 refunds,
6,000 bad addresses,
5 new marketing lists,
40 returned shipments,
30,000 credit apps,
2 million transactions, and
1 million duplicate names.

While we joke about the enormity of it all, if you are a retailer stumbling under the weight of all this data, there is hope and over the next few weeks we’ll dive a bit deeper into these figures to showcase how you can get control of the incoming data and most importantly, leverage it in a meaningful way.

Sources:
http://techcrunch.com/2013/11/29/black-friday-online-sales-up-7-percent-mobile-is-37-percent-of-all-traffic-and-21-5-percent-of-all-purchases/

http://www.pfsweb.com/blog/cyber-monday-2012-the-results/

http://www.foxnews.com/us/2013/11/29/retailers-usher-in-holiday-shopping-season-as-black-friday-morphs-into/

When Direct Marketing Fails…

As a Direct Marketer in a tough economy, you are constantly being asked to do more with less.
Generate more leads. Improve campaign results. Increase brand awareness. Streamline operational efficiency. Unfortunately, poor quality customer and prospect data can sabotage your direct marketing efforts and your reputation in the process.

Want to get a handle on the ways that data could be tripping you up? Here’s a quick look at the top 5 Ways Bad Data is Hurting Your Direct Marketing Efforts AND how you can start to turn that trend around…

1. BAD DATA Wastes Money

In this most obvious example, companies with large, unkempt databases ‘pay’ for bad data, directly and indirectly. How much? Duplicate records mean duplicate print costs and double the postage but half the potential return while invalid or outdated addresses and garbage data is like throwing money out the window. Even in the digital world, bigger databases cost more money. Email vendors, marketing automation vendors and database marketing companies typically charge by volume of records – so larger, unclean databases will cost you more regardless of your marketing strategies.

2. BAD DATA Reduces Response Rate

While bad data has the potential to waste significant dollars per campaign, the flip side of that coin means your campaign response rates are compromised. In other words, when you send that direct mail campaign out, even if you get tons of responses – they are always divided by the ‘potential’ of your entire database (response/total sent). As a direct marketer (whether you use email, direct mail or telemarketing) the only way to demonstrate the maximum return on investment is to remove duplicate records, update old addresses and eliminate anyone who shouldn’t receive your marketing (which could include unsubscribers, gone aways or deceased records).  Now that return rate is an accurate reflection of your skill as a marketer and you wasted a lot less money in getting there.

3. BAD DATA Means Bad Targeting

How well do you know your customers and prospects? How many unique customers are in your CRM? Do you know how much they spend? What they like to buy? Where they shop? If you don’t – it means your direct marketing initiatives are being held back by a lack of information.  The remedy? Put all your customer data in one place, accurately entered and complete. This might mean merging contact data with household income. It might mean linking all their product purchases so you can determine buying habits. It might mean knowing ALL their addresses but choosing a master file so you know how and where to target them appropriately. For retailers this is known as a Single Customer View and it allows you to simply know more about your customer so you can use it to maximize your direct marketing efforts.

4. BAD DATA Hurts Your Reputation

Companies that market well have a better reputation. This needs no data to support it – simply check your kitchen counter for the catalogs and marketing pieces you’ve ‘saved’. What do they have in common? They were all mailed accurately (they made it to your mailbox) and they promote products that interest you (as compared to that motorcycle catalogue you may have tossed). And they showed up at the right time (like that home improvement coupon that arrived just after you purchased your new house). So to protect your marketing reputation you want to suppress and organize your data so that you don’t mail to the wrong people at the wrong time with the wrong message. Remove gone aways, deceased and anyone who does not want to get your mail. Your customers will thank you in the long run by continuing to purchase from your business (and not writing nasty things about you on Twitter). In addition, your ‘green’ credentials will soar by taking the initiative to reduce wasted mail.

5. BAD DATA Risks Opportunities

Almost everyone knows that Marketing and Sales go hand in hand and it’s all related to good data. When sales reps and account people have the correct information on the screen, they can go the distance in selling and up selling to prospects and customers. They can waste less time calling people that are not relevant and spend more time focusing on the ones who are. They can spend less time on each call searching for data or recollecting it and instead, wow the customer with how organized and efficient their interaction is. Accurate and complete data improves sales and wastes far less opportunities.

Mounting evidence suggests that cleaner, more accurate data has a direct impact on your direct marketing success and each year, more and more companies make data quality a key focus in both their marketing and technology departments.  Removing duplicates, updating addresses, gone away/deceased suppression and generating that all-important single customer view will allow you to

So what’s a Direct Marketer to do? Make every effort to regularly dedupe your database or CRM to keep the size reasonable and reduce waste across all your marketing initiatives. It’s also advisable to validate addresses and suppress gone aways and deceased records before a major mail campaign to reduce the waste associated with excess mailers and postage.  To take your Direct Marketing to the next level, work with your technology or database team to establish a comprehensive Single Customer View. This will allow you to use your existing customer data to drive better and more targeted Direct Marketing campaigns. You can also supplement existing data with a wide range of additional datasets. Lastly, keep good track of customers and prospects who unsubscribe or who don’t respond to specific types of mailings so you can market to only those receptive to your offer.

If you are interested in learning about how helpIT systems data quality toolset can address some of your Direct Marketing challenges, please feel free to contact us for a Free Consultation and Trial of our matchIT data cleansing software.

Data Quality Makes the Best Neighbor

So this week’s #dataqualityblunder is brought to you by the insurance industry and demonstrates that data quality issues can manifest themselves in a variety of ways and have unexpected impacts on the business entity.

Case in point – State Farm. Big company. Tons of agents. Working hard at a new, bold advertising campaign. It’s kind of common knowledge that they have regional agents (you see the billboards throughout the NY Tri-State area) and it’s common to get repeated promotional materials from your regional agent.

But, what happens when agents start competing for the same territory? That appears to be the situation for a recent set of mailings I received. On the same day, I got the same letter from two different agents in neighboring regions.

Same offer. Same address. So, who do I call? And how long will it take for me to get annoyed by getting two sets of the same marketing material? Although it may be obvious, there are a few impacts from this kind of blunder:

  • First of all – wasted dollars. Not sure who foots the bills here – State Farm or the agents themselves, but either way, someone is spending more money than they need to.
  • Brand equity suffers. When one local agent promotes themselves to me, I get a warm fuzzy feeling that he is somehow reaching out to his ‘neighbor’. He lives in this community and will understand my concerns and needs. This is his livelihood and it matters to him. But, when I get the same exact mailing from two agents in different offices, I realize there is a machine behind this initiative. Warm feelings gone and the brand State Farm has worked so hard to develop, loses its luster.
  • Painful inefficiency.  I am just one person that got stuck on two mailing lists. How many more are there? And how much more successful would each agent be if they focused their time, money and energy on a unique territory, instead of overlapping ones.

There are lots of lessons in this one and there are a variety of possible reasons for this kind of blunder.  A quick call to one of the agents and I learned that most of the lists come from the parent organization but some agents do supplement with additional lists but they assured me, this kind of overlap was not expected or planned. That means there is a step (or tool) in the process that is missing. It could require a change in business rules for agent marketing. It’s possible they have the rules in place but requires greater enforcement. It could just be a matter of implementing the right deduplication tools across their multiple data sources. There are plenty of ways to insure against this kind of #dataqualityblunder once the issue is highlighted and data quality becomes a priority.