After the lessons we learned in the Band 1Â and 2 OptimizationsÂ (see Band diagram) for HSN,Â we were able to reallocate that budget toÂ invest in Band 3 – Optimizing the Interface.Â We realizedÂ during the previous Optimizations we were already getting a tremendous amount of traffic throughÂ channel surfer / clickers, but this trafficÂ was not “converting”.Â Â In other words, we really needed to Optimize the â€œLanding Pageâ€Â for this existing audience – the TV show itself.
Don’t suppose the aboveÂ scenario sounds at all familiar to the web analytics folks out there – you know, “more traffic, any traffic” is the answer?Â Oops, maybe not, what aboutÂ higher conversion?
I won’t bother providing the Band 3 example for the web; youÂ all know what Optimizing Landing pagesÂ /Â web sites is about, or can certainly find that info elsewhere.Â However, you might find the Optimization of a TV shoppingÂ channel interesting…
A little background first.
You probably don’t know how interactive HSN was, at least back then;Â the showÂ just looks like somebody talking about products.Â What you don’t know is there is a huge multi-screen war room setupÂ steering the ship.Â Every competitor monitored live in real time, along with allÂ major TV networks.
Somebody is burning a flag on the steps of the Supreme Court, and every TV network is covering it?Â Start selling American flags.Â Folks on the Today Show say “blue is the new black”?Â Put on blue clothing all day.Â Competitor selling a product we have in inventory?Â Put the same product up at a lower price.Â The war room was a wild place – and especially so with 3 of these networks, all live, going full blast at the same time, 24 hours a day!
So first, HSN was interactive with the “environment”, followed trends rather than trying toÂ Market against them.Â Â Customer demand (sales per minute) declared what to put on a show; if they wanted to buy it, we wanted to sell it.Â If they didn’t want to buy it, we tried to get rid of it by cutting price.
Sound familiar?Â A bit like keyword phrase /Â Search analysis, perhaps?
HSNÂ was also interactive with customers.Â ThereÂ are the callers you hear on TV, of course.Â What you probably don’t know is allÂ TV Shopping customers are absolutely chock full of comments about the products and presentations, and these are tracked and reported on the very next day.Â Every day we had a sales meeting to review the prior dayÂ – how could we have done better -Â and as part of this meeting, we reviewed customer comments from the previous day, andÂ tried to take action on these comments.
To put some context around these dailyÂ sales meetings, the PresidentÂ of the company ran the show, andÂ the attendees were all VP or above.Â It was the most important meeting anybody had.
The kind of meetingÂ a company might start havingÂ to really leverage the power of Interactivity andÂ the output of Social Media,Â perhaps?Â We’re talking aboutÂ close to a billion dollar in sales company at this time, not a start up.Â We sold management on the importance of an Analytical Business Culture.Â You can too.
So, we have this pretty interactive system despite the one-way technology of TV, and Marketing now has budget to try and Optimize it.Â What would you do, how would you improve this?
Here’s where we ended up, in some casesÂ after years of working through the testing:
1.Â Feed customer comments from the call center to the show host live.Â We invented a system to send customer comments in real time to the host of the show.Â They scrolled on a screen as the hostÂ was pitching the item, and could be tremendously effective in improving sales.Â When I heard of people Twittering about speakers at conferences,Â that reminded me very much of this functionality.
2.Â Capture successful interactions in the product database for review later.Â After the show, the hosts wouldÂ enter notes on which customer comments “worked” for specific products and allowed them to sell at a higher velocity.Â These “best of” ideas could then be reviewed by any host who would be selling the product.
3.Â Make real time testing actionable in the future.Â There was a lot of testing in real time besidesÂ the host pitchÂ – changing displays, lighting, formats, etc.Â However, there was no “system” for capturing these ideas, it was all “gut” and learned by practice.Â So we did the same as with the comments above – gotÂ these “tips and tricks”Â loaded into the product database so show prep folks and producers could get a leg up using the experience of others.
Not exactly A / B testing, but there’s no A / B in live TV, you have to look at historical performance in the same Dayparts – and we did.Â More on this in the next initiative…
4.Â Program to the audience.Â The very difficult thing with selling product onÂ TV Shopping versus the web, of course, is that you are selling one product at a time.Â How do you Optimize that?
We studied the buying patterns – rowing machines sell best on Sunday mornings, Fashion on Tuesday nights, kitchen stuff at 3 PM weekday afternoons.Â In other words, Dayparting.
The audiences change throughout the day, and they have a different compositionÂ on Weekdays versus Weekends.Â Optimize the product presentation to the audience.Â Using Nielsen ratings for this Optimization would be a “Push” approach.Â By using buying behavior, we were using a “Pull” approach – sell them what they have told us they want to buy using their actual behavior.Â By the hour.
5.Â Implement the best performanceÂ metrics and controls.Â In the early days, Gross Sales per MinuteÂ were everything.Â Â Analysis proved that return rate and price had a very high correlation, which in this case wasÂ indeed causation -Â and we proved this with price testing.
So we came up with the idea of an “average price per show” which would minimize return rates based on Daypart.Â Literally, we found that the time of day a high priced product was sold influenced the return rate.Â This was an improvement, but the system could be gamed by smart hosts looking to drive bonuses.
So, we converted from Gross Sales per MinuteÂ targets to Net Sales per Minute targets – net of returns, actual if available or forecast based on like item history, all inÂ real time.Â But in the case of some high volume categories with thin margins, this was not Optimal either.Â So ultimately we made it to Net Gross Margin per Minute as the final sales metric.Â Very similar toÂ moving from ROAS to ROMI.
This series of “Business Rules”Â linking price, return rates, margins,Â and time of day basically ensured that even in a completely dynamic, real timeÂ environment, the whole system was Optimized for profit.
Do you wonder if a Dayparting approach could improve your commerce profits on the web?Â Based on what I’ve seen, I’m pretty sure it would, as long as the costs of implementation are not prohibitive.
Look at these 5 ideas above.Â Ask yourself ifÂ any of this is what you think of as “Marketing”.Â TheseÂ ideasÂ are in reality a series of very intensive IT projects (on MainFrames!) involving telecommunications, core systems architecture, programming / code, and more.Â Why was Marketing involved?Â None ofÂ this is aboutÂ “Creating Ads”.
Marketing was involved because we insisted on being involved.Â In an Interactive system, you can’t OptimizeÂ Marketing until you Optimize the systems delivering the Interactivity.Â Â Â Those of you Optimizing web sites are quite familiar with this concept by now.
It’s about removing Friction.
Said another way, these ideas were all directly related to Voice of the Customer, Customer Experience, and long-termÂ Customer Satisfaction.Â The better HSN was on these issues, the more successful Marketing programs would be in terms ofÂ increasing Customer Value.
Engineers are smart people, but if they knew everything,Â then why would webÂ sites,Â VRU’s,Â orÂ countless other machine-human interfaces have to be Optimized?Â For best results, you really need both brainsÂ working together on theseÂ systems from the beginning.Â Â iPod / iTunes / MacÂ isÂ theÂ most recent example – the Marketing Strategy is built into the product from the very beginning.
Failing this ideal world (and many companies are not there yet, for sure)Â analysts must getÂ these Interactive systems OptimizedÂ before addressing the nextÂ challengeÂ – OptimizingÂ Customers -Â which begins in Band 4.Â Otherwise, Marketing success will beÂ undermined byÂ Friction instead ofÂ driving incremental Customer Value.
For more on Measuring and Managing Friction, See Chapter 5 in the book sample PDF.
Any questions on the approaches / testing / decisions above?
(A post by postÂ index of this Marketing Bands Series is here.)