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Posts Tagged ‘word of mouth marketing’

PostHeaderIcon Social Media Channel Architectures – Part 2

We left off last time having defined a conceptual approach to channel architecture with an example of a new type of soap – “Greensoap” which has three unique advantages: it sends fewer harmful chemicals into the water supply, it doesn’t get mushy when stored, and it is 50% cheaper than other leading brands.  How do we leverage a social media channel architecture for this product launch?

Following the model (and in fact basic marketing 101), we first need to know what audiences we are targeting.  In this case there are three which can be defined based on attitudes and behaviors:

  • Green Consumers.  Green consumers most important attitude is that they believe the environment must be protected, that current economics doesn’t measure the “true cost” of products, and that if the true cost were available we would see that dumping chemicals into the environment (and later having to remediate) is more expensive than just selling a green product in the first place.   They are split evenly between men and women, predominantly 18 – 35, and average income of $45,000/year.  Their behaviors: they tend to shop at smaller stores with a focus on environmental sensitivity, they are relatively price insensitive up to a 20% price increase over non-green products, and they tend to be vocal in online communities around green issues.
  • Vacation Travelers.  These folks tend to bring soap rather than use what is in the hotel room because their stays are longer and they travel with their families of 2.2 kids (whereas business travelers stay short periods, want to travel as light as possible, and so use in-room soap provided by the hotel).  This audience is mainly women 30 – 50 and is concerned with minimizing the burdens of “household  care” – meaning keeping a clean house, clean kids, and organized environment.  75% have a job, are incredibly time constrained and stressed.  They tend to shop at one store, usually a major grocery chain outlet between work and home.  If a product makes their life one iota easier, they will consider it.  They are highly swayed by friends and family validation that a product meets its promises.  Once they try a product, they are incredibly loyal up to a price premium of 25% over their current brand.
  • The Thrifty Shopper.   This buyer always worries about money and saving it is their first priority.  Split equally between men and women, the demographic is flat across all age groups, with a slight peak within 60+ years groups due to their fixed incomes (+ life experience during Depression and WWII).  This buyer shops in big box stores and low-cost chain grocery stores like Safeway and Savemart.  No product loyalty whatsoever – they’ll switch brands for as little as a 5 cent savings. 

The next step in the model is to figure out what kinds of media these audiences use and where they are likely to be on the web.  Figure 1 shows the mapping of audience to media, platform, and social media sites.  The sites listed are intended to be category “examples” – meaning that they are only one potential site that could be used.  For example, in column 2, cafemom indicates women-focused social media networks. 

 Figure 1
Mapping Prospective Audiences to Social Media Channel Categories

Mapping Example Audiences to Social Media Channels

The third step is to define our messages to each audience (if we haven’t already done so).  For purposes of this example, we’re going to keep this to one message per consumer segment:

  • Green Consumers: Greensoap leaves you AND the world a cleaner place.
  • Vacation Travelers: Greensoap keeps your family clean and your life simple.
  • The Thrifty Shopper: Greensoap keeps you clean and green at 50% of the cost of other soap.

In the next post we’ll put together the campaigns and then show how we apply them to the various media channels.  Stay tuned.

 

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PostHeaderIcon The Economics of Twitter for Advertisers, Part 2

Let’s continue our discussion of Twitter economics.

The average Twitterer has 549 followers.  Now this is skewed by corporate accounts (e.g. like our travel sites) and news sites that have a very large number of followers.  I have gone through a number of accounts to determine what seems like a realistic average number to use – and I am going to assume 200 followers.  Our experience is that for the first generation of followers, 10% pass along an offer (the theory of this is also quite enlightening but I will not cover it here).  For subsequent generations it is much lower, usually in the 2-5% range.  We mentioned previously that 15,000 is the average number of followers for the Big 3 sites (Expedia, Orbitz, Travelocity).  The calculation therefore looks something like the following:

15,000 (followers)

+ (15,000 * 200 *.1) = 300,000 (first generation pass along)

+ (300,000 * .02 * 200) = 1,200,000 (second generation pass  along)

= 1,515,000 (total number of individuals)

The number of impressions is then this base of 1,515,000 multiplied by the number of offers “seen”.  Expedia seems to be making offers every five minutes, as does Hotwire  (they must have set up some kind of automated feed into their Twitter accounts).  Travelocity and Orbitz seem to be making offers once a day (or even less).  The big unknown is how many offers does the average follower actually see?  They aren’t always online, or if online, they are doing other things and their attention is not focused on Twitter.  Or they are on Twitter, but the offer doesn’t register through the noise of all the other tweets.  Without any really good data, I will assume that each individual “sees” two offers/month – which I hope is a conservative number.

This means that the total number of impressions is: 1,515,000 * 24 = 36,360,000 per year

Given this number of impressions, what is the potential economic impact for Expedia, Orbitz, and Travelocity?  Typical conversion rates on these sites runs 3-5% according to various published data I have seen.  But, this is not a situation where someone has either typed in a keyword or clicked on an ad that appears when a keyword is typed in.  This is much more of a grazing situation.  Many offers are made, but only a few are relevant to any specific individual.  So the response rates look more like email, and yet they are even smaller.  Why?  Because while the first generation is signed up to receive notifications (parallel notion to an email, in this case), the second and third generation are not.  Our first benchmark is therefore an email conversion rate from the initial mailing – which is calculated as  follows (I am ignoring losses due to bad addresses, since that is not an issue for online accounts - although see below for a related issue of dormant accounts):

# of impressions * open rate * conversion rate

Typical average open rates for good emailings are 10-12%, and conversion rates vary but let’s assume 2%, which is a number that comes from my experience with emailings.  That would yield the equivalent of a .2% conversion rate for the first generation.  But for the second and third generations, the response would be substantially smaller, maybe .1% or even as low as .05%.  Since the first generation is such a small number of individuals, I will use .1% as the conversion rate for the entire base of impressions.

The last pieces of data we need are the number of tickets purchased, the number of purchases per individual in a year, and the average revenue to the travel agency from each ticket purchased.  Again, I am going to use data that is fairly well known in the travel business.  These are gross averages and do not take into account a number of variables, such as the type of travel (business vs. personal), destination (domestic vs.international), and type of flier (managed vs. unmanaged)

Number of trips per year: 2

Average number of tickets purchased/trip: 2.2

Avg revenue per ticket to agency: $25

So now let’s do the annual revenue calculation for the economic impact of Twitter for a large online travel agency:

36,360,000 * .001 *2 *2.2*25 = $3,999,600

 

For a big travel agencies, which have around $1B in annual revenue, this is small (.4% of revenue) but it isn’t chump change either. 

Before I close, one other issue needs to be explored – and that is the issue of dormant accounts.  The model presented assumes that every individual who is following or who receives a retweet or direct message is an “active” Twitter user.  But as we all know, many from our own experience, you may set up a Twitter account and then never go back to it.  Or you may visit it only rarely.  I call these dormant accounts.  There has been a lot written on this topic – just type “dormant twitter accounts” into Google.  Nicholas Carlson recently wrote a post for BusinessInsider.com titled “60% Of Twitter Users Quit After A Month“. Carlson cites Oprah (@oprah) as an example of someone who has become “bored” with Twitter and reports that Nielsen Online estimates that 60% of Twitter users quit after a month. The post goes on to say that the 60% number may be misleading as Nielson only measures Twitter usage based off Twitter.com and not from mobile use or apps like TweetDeck.  Given this data is pretty consistent with other social media sites, and the fact that a lot of tweets happen off of twitter.com, I think we can safely assume that the dormancy rate for Twitter is 50%.

In this case, our approximately $4mm in annual revenue has now become $2mm in annual revenue. 

Not huge, but I think we could say that the ROI on the costs associated with maintaining a corporate Twitter account for this purpose are probably pretty spectacular. 

I do not doubt that this post will cause a lot of discussion/controversy (at least I hope it will), and I look forward to all feedback. 

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PostHeaderIcon The Economics and ROI of Twitter for Advertisers, Part 1

All I hear about is what the value of Twitter is (hopefully) to investors. What is Twitter’s business model?  How will it make money? As a business person, I really don’t care about how much Twitter’s founders and investors will make (which is no doubt a heck of a lot more than I ever will). I care about my favorite radio station – WIFM – better known as What’s in It For Me?  The two questions are not unrelated.  For Twitter to make money, it will almost certainly need a base of advertisers who want access to it’s audience.  There may be other revenue streams that the creative minds at Twitter will conceive over time, including some form of CPM, CPC, CPA or CPL.  That advertising opportunity, however, does not exist on today’s Twitter.  Yet, advertisers are trying to leverage Twitter now to increase sales. 

Is there a way to model the ROI from investing in a presence on Twitter as it exists today?  Let me suggest that there is and provide the approach and calculations below. 

First, we need to understand what Twitter is and how its audience uses it.  I view Twitter as multithreaded Internet chat.  It’s like being in a coffee house with conversations going on all around you and choosing which ones you want to participate in.  Moreover, the form of communication – 140 characters – lends itself mainly to status updates and quick bursts of timely information.  Twitter is at its best when it is used to communicate information whose value deteriorates at  a rapid rate.  It particularly does because the information is streamed – and the stream flys by so fast that anything much older than a few hours is effectively lost unless you actively search for it in historical tweets – which is a change in consumer behavior that few have associated with Twitter yet.  Thus, Twitter in its near real-time form is perfect for businesses and business models whose information quality degrades quickly – e.g. stock prices, airline ticket prices and availability, exploding special offers/deals (an offer that has a specified end date), employment opportunities, immediate local expiring opportunities (e.g. ticket availability at the stadium just before a big game), among others.  

So let’s say you are an online travel agency that sets up and maintains a Twitter account.  Do you care?  Is it worth the effort to put specials out through that mechanism?  Let’s look at some numbers.  Here are the number of followers of various travel agency Twitter accounts:

  • Expedia – 13,281
  • Orbitz – 14,087
  • Travelocity – 16,133
  • Cheapoair – 1,925
  • Vayama – 1,193
  • Travelzoo – 8,020
  • Priceline – 16,212
  • Hotwire – 3,769

Assume that you have a Twitter account with 15,000 followers (the average of the big sites) where you post daily specials on travel.  They are obviously interested in the opportunities – so we have an audience that, relatively speaking, is highly motivated to purchase if they can find the deal they want.  As active participants, they are likely to forward information to friends and family who have a similar interest – so they retweet or they forward via direct message.   I call this the amplification effect.

The key question to consider in this is what percentage of people pass the information along and then, subsequently, what percentage of people subsequently retweet to the next level?  We actually have good data on this from word-of-mouth marketing campaigns we have run for some of our travel industry clients and from Twitter follower data.  There is also research (see Norman, A. T., and Russell, C. A. (2006). The pass-along effect: Investigating word-of-mouth effects on online survey procedures. Journal of Computer-Mediated Communication, 11(4), article 10. http://jcmc.indiana.edu/vol11/issue4/norman.html) that aligns well with our experience.

More in the next post.

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