I bought a dress because of your Facebook ad, but you may not know it

A model walks down a fashion show runway in a red and black dress
“Stop Looking! Fashion Runway 2011” by Henry Jose, via Flickr Creative Commons is licensed under CC BY 4.0

I recently bought a dress online following this flow:

  1. See dress on a Facebook ad, fall in love with it, click on ad
  2. Ad takes me to a company page, I’ve never heard of the company before, this makes me wary of purchasing
  3. Conduct a Google search for reviews of dress
  4. Finding nothing, go to Amazon and look for the dress there. Find positive reviews, including photos of actual people wearing the dress
  5. Opt to purchase on Amazon because:
    1. Amazon has standardized recourse/return methods if the purchase goes bad
    2. I can easily track the shipment
    3. I had a gift card from my birthday I wanted to use up
    4. It was the same price as the initial website

If you’re the business selling the dress, using simple Click-Through Rate (CTR) tracking methods (# of people clicked on ad, % purchased after clicking), you’ll never know that the Facebook ad “worked.”

If you’re using “Last Interaction Model” tracking, you’ll assume the purchase came from Amazon. Amazon played a role, but it wasn’t the whole story and didn’t prompt the purchase.

If you’re using “First Interaction Model” tracking, you’ll assume the Facebook ad did all of the work, ignoring the role of the web search and Amazon.

To really understand the full journey, you have to look at a broader set of data and how various advertisements and marketing promotions play critical roles in your sales.

 

Further reading: Addressing the Question: Measuring Advertising ROI

 

Use your marketing skills to analyze your own spending habits (and save money)

Using this same framework, you can analyze your own spending habits and find what motivates you, what messages work on you, and how you might be able to change your spending habits to save money.

If you’ve studied marketing and advertising, you’ve very familiar with the analysis of potential and current customers.

When analyzing our target market, we ask:

– Who are they?

– What products are they most likely to buy?

– What messages are they most likely to respond to?

– What motivates their purchases?

– What causes them not to purchase?

Using this same framework, you can analyze your own spending habits and find what motivates you, what messages work on you, and how you might be able to change your spending habits to save money.

Two ways to get started:

Analyze your Amazon suggestions/purchase history

Amazon (or any other major online retailer) spends significant resources to understand your spending habits and predict what you are most likely to buy next. Why not use this to your advantage?

For example, a quick skim of Amazon’s suggestions for me indicates that I’m most likely to buy beauty products and kitchen gadgets from them. This makes sense, as I’m very particular about wanting a specific beauty/kitchen product and unwilling to go to 10 stores to find it. At the same time, beauty products can be more expensive on Amazon than in retail. I could save a significant amount of money by going to a brand’s website and finding the products locally in a store. Or, I could save money by being less particular with my purchases.

Analyze your debit/credit card statements

Take look at your debit and credit card statements from a third-party perspective, as if you were analyzing someone in a focus group for your product or service.

What are you spending your money on and where? What’s the repetition of your spending habits? Where are the patterns? What percent of your money is going toward various purchases or categories of purchases?

For example, after I gifted him The Total Money Makeover book by Dave Ramsey, a friend of mine analyzed his own budget from a third-party perspective and what he found was shocking: “The family” (aka him and his wife) were spending $1,400 per month on eating out!

So dedicate some time this week or weekend to taking a look at yourself as a target market and see where you spend your money and how you might change that for the better. 

Common rewards program errors and how to avoid them

Last week, Ad Age published a great article, Ad Age Best Practices: How to Create a Rewards Program That Really Works. I highly recommend reading it. The one thing the article didn’t cover is common rewards program errors and how to avoid them.

Common error: Offer only BOGO discounts when your target market is mostly single

I jokingly nickname these rewards programs “Non-lonely people discounts.” There’s nothing wrong with Buy One, Get One Free  (BOGO) if your main target market is married, but with the the majority of US adults being single, I don’t recommend rewards programs based on BOGO discounts. It’s not that single people can’t grab a friend and take advantage of it, but this creates another step for them to do, which definitely lessens the reward.  You can have some of these as part of your rewards programs, but you should also include other non-BOGO rewards as the majority.

A Nike gift card that has
“Reward Your Champion.” by Arne Krueger, via Flickr Creative Commons is licensed under CC BY-SA 2.0

Common error: Give rewards that consumers can get other ways without having to do anything

When Verizon Wireless announced that they were going to be providing a rewards program, Verizon My Rewards+, I was excited; I’ve been a loyal Verizon customer for years. The problem was, when I went to see what I could get with my rewards points, it was basically discounts I could  already find somewhere else.

For example, it’s not hard to find 10% off discounts on gift cards to dining and shopping category stores through coupons, special promotions, etc. But Verizon’s “reward” program is:

$100 gift card for dining or shopping chain (such as Texas Road House) – 1,000 rewards points = $90 (10% off)

Groupon and LivingSocial have better deals than that!

In comparison, my credit union offers full gift cards (not discounts) for using their debit card. In one year, I accumulated enough points to get a $50 Amazon gift card and a $25 Starbucks gift card completely free.

What makes this example even worse is, when I sent feedback on the program to Verizon, (at their prompting) all I got back was a form letter/macro about how amazing their rewards program was.  Whoops.

Common error: Get creepy with data collection

I think most people understand that companies use rewards programs to collect consumer data (at least, I hope they do). But one thing is for certain, people don’t like to be reminded that a company is using the data to drive more sales.

For example, it’s ok for Walgreens to remind you that you need to refill your prescription or Amazon to suggest other movies/music you may like, but it’s not ok for Target to predict the pregnancy of a teenage girl. If you are going to use the data to promote items to your consumer base, be careful, make sure that the use of the data has some direct benefit to them.

What rewards fails have you seen? What rewards programs are your favorite?

Age vs. Generation: Choose the correct metric for marketing and advertising

A young girl staring at an Apple Computer
“…next generation” by zeitfaenger.at, via Flickr Creative Commons is licensed under CC BY 2.0

AdWeek recently published an article and infographic comparing what Millennials and Generation Z want from brands. In reality, reports like this are a comparison of age groups, not generations, and should be used only to impact decisions right now, not long-term.

As consumers age, their brand preferences, media preferences, privacy concerns, priorities, etc. change. So, to say a 13 year old’s preference for YouTube and a 30 year old’s preference for Amazon is because of what generation they belong to isn’t accurate. More likely, these preferences are based on their age.

Are generational studies useful? Yes! If you are communicating with the generation right now, the AdWeek Infographic and Deep Focus study can be incredibly helpful in developing marketing and advertising campaigns (although I’d like to see a larger sample size).

Generational reports also provide some interesting indicators as to how their preferences will change over time for predictive purposes. But to do this accurately, you’d have to look and see how previous generations have changed over time as they aged for comparisons.

As with all market intel, think about your end goal, and then work backwards from there to find the right intel to use.

Photo: “…next generation” by zeitfaenger.at, via Flickr Creative Commons is licensed under CC BY 2.0