The WOM Factor – Why ALL eBay Sellers are Not Equal
I’ve answered too many eBay’er questions over the years, its probably why I have such a tainted view on specifically eBay buyers, my personal ‘resounding’ conclusion is that they’re scared and really scared at that, almost to the point of paranoia.
In the next few minutes, we’ll be looking at the different types of buyers and a new system of gauging sellers, not feedback, but a derivative of feedback, which gives a clearer overall picture of the seller in proportion to their customers views and perception of the business. I’m calling this the WOM factor.
Multi-Channel
For multi-channel businesses, they’ll know that there are three different breads of customer and they vary enormously. These are:
#1 The Website Buyer
Website customers are the most relaxed of them all, you’ve woo’ed them with your marketing and reassured them with your subliminal security and reassurance factors. They’ll be happy with a couple of days shipping time and normal have already answered their questions before even buying from you.
#2 The Amazon Buyer
Its extremely rare to receive a question from a Amazon buyer and if you do its 99.9999999999999999999999999% of the time related to shipping or a broken item. Besides that, they’re quiet as a mouse.
#3 The Psycho eBay Buyer
I feel sorry most of all not for the sellers on eBay, but for the buyers. Mentally unhinged, these buyers are the stuff of customer services nightmares, they’re scatty and nuts, they’re lunatics and time wasters. But most of all they’re just scared.
They’re scared of being ripped off, they’re scared you’re going to steal their money and emigrate to Nigeria and then sell all their personal details to a chap in a mud hut who will spawn 10 versions of themselves as the victim.
Harsh, but that’s the kind of metal thought patterns that go through these paranoid buyers heads
Word of Mouth
This isn’t a new concept, infact its a very well documented concept, in short it simply says, that for a good experience a customer will tell two people and for a negative experience, they’ll tell 10 people.
Loss & Reward
This also sits well with the experiences of reward and loss. If I take your pet cat called “puddles” away from you right now, never to be given back, the sense of loss you will feel will be immense; However if I give you back a kitten called “Spot”, you’ll learn to love the little ball of fluff, but it will never replace puddles, who’s loss carries a far far greater sense of loss, than any gain can give.
Also word of mouth is extremely strong, companies are scared of the extremes that can occur with terms being coined such as ‘Brand Terrorists’, those customers that have been so pissed off by a business or brand, there is no stopping them ram-raiding the company or brand at any opportunity.
Inversely, when tribes are formed (Seth Godin’s input here) “Brand Sponsors” are created, those people that are just nuts for a product or service, the most immediate example I can think of are Apple fans. I’m an Apple product fan, but I’m to the level of what I would call excessive, that some of these Apple nut-mini-Steve-Jobs are.
Using “Word of Mouth” to Measure eBay Seller’s
Its not hard to see why either, if we use the rule of that one positive comment will create 2 positive word of mouth reviews and a negative or neutral comment -10 word of mouth reviews, then its not hard to do the maths on a random selection of sellers and understand that eBay’s growth is actually tainted by its underlying feedback system and also that all sellers are not actually equal.
The DATA – Random Sellers Feedback
These were taken completely at random, I picked four categories and picked a couple of sellers for each category from the top of the list (yes I’m aware this is weighted by the best match algorithm) and included their feedback for the past 12 months and neutrals are counted as negatives.
I have not included revised feedback, I could not decide whether these were positive or negative events, so have elected to ignore them completely. If I was forced to add them, I would class them as a negative event, as it was not a “perfect” transaction, perhaps I should look at this again in a few weeks and maybe I’ll attribute a +3 or +4 to these, but for now, I’m not sure.
PS: What do you think? Post in the comments below!
Random eBay Sellers Feedback Scores
ID | Positive | Negative | +Points | -Points | WOM Factor |
jpe_enterprises | 189 | 2 | 378 | 20 | 5.29 |
loco_gadgets | 378 | 1 | 756 | 10 | 1.32 |
benthamltd | 80791 | 1008 | 161582 | 10080 | 6.24 |
argos | 259217 | 6181 | 518434 | 61810 | 11.92 |
xia090729 | 561 | 7 | 1122 | 70 | 6.24 |
glamorousoutlet | 22585 | 530 | 45170 | 5300 | 11.73 |
bench_outlet | 40274 | 530 | 80548 | 5300 | 6.58 |
bessy0302 | 2960 | 12 | 5920 | 120 | 2.03 |
online4babyltd | 55870 | 614 | 111740 | 6140 | 5.49 |
babzeeonline | 19549 | 217 | 39098 | 2170 | 5.55 |
tennis-deals-2008 | 4221 | 24 | 8442 | 240 | 2.84 |
poshtotz-store | 5337 | 34 | 10674 | 340 | 3.19 |
little-devils-direct | 773 | 10 | 1546 | 100 | 6.47 |
flyingplaneman | 4765 | 68 | 9530 | 680 | 7.14 |
kmsdirectshops | 14069 | 198 | 28138 | 1980 | 7.04 |
aqua_spot | 894 | 7 | 1788 | 70 | 3.91 |
Totals | 512433 | 9443 | 1024866 | 94430 |
Understanding the Data
I’m quickly adding that several of these sellers actually had either 100% or 99.9% feedback scores, this is only one factor that I am indicating in this article. While the vast majority of these sellers are above 99.0% feedback, Argos stands out for two reasons:
- They have a feedback score of 98.7, the lowest of the group
- They have the worst ratio 11.92% of WOMF
The second, is on face value an OK seller, they have a score of 99.1% currently, which is good enough and almost all retail stores in the physical world, would probably never be able to achieve this.
Glamorousoutlet are turning over a decent amount of items, with 22,585 feedback in the last year, this is probably around +32,000 orders, however they have incurred 530 negatives, or using the WOM Factor a negative score of 5300, giving them a WOM of 11.73 which when you look at Argos with their 98.4% feedback, is actually worse in proportion!
How to Calculate the WOM Factor
Calculating this is easy, you take your positive feedback for a set period of time and times it by 2, then you take the negative and neutral comments and times them by 10. Then divide the negatives by the positives and times by 100 to gain a more friendly number. In short the lower the better.
What Customers Really Think
Being able to gauge what your customers truly think of your business is stuff of marketeers wet-dreams. This new factor, I’m coining as the “WOM Factor” can be one tool in your arsenal to accurately gauge what your customers actually think of you.
To give you a measurable and a new dimension on what is just raw numbers. The WOM Factor gives you an indication of what is the actual effects and general response of your business on the outside world.
I wonder what the WOM Factor for Microsoft is?
I wonder what the WOM Factor of Apple is?
I wonder what the WOM Factor for the entire eco-system of eBay is?
Whats Your WOM Factor?
This leads to the pivotal question, whats your WOM Factor?