Really interesting design experiment from Chinese university students. It is interesting that they use the ‘goldfish’ as the avatar of the AI. It also asks questions about how we relate to pets and whether augmentation like this would work.
Very interesting student project from Shanghai Jiaotong University, has your pet fish serve as an avatar/front end for a smart device pic.twitter.com/tHDODQHArM
This Amazon advertising pitch deck is interesting for a number of reasons
They claim that Amazon advertising data taps into consumer purchase behaviour both on and off Amazon – that would be of interest to anti-trust regulators
Otherwise the Amazon advertising seems to have both Facebook and Google advertising offerings squarely in its sights. This will be attractive for many brands that are looking at having a DTC route to market over the coming years.
People aren’t that excited for iPhone 8, Piper Jaffray says | CNBC – it makes sense, smartphones are a mature device now, the form factor is set (not sure this a good thing), the app market has peaked and the hardware commoditised. You’d buy a new iPhone when your old one is no longer fit for purpose
TalkTalk looks to hang up on its mobile business | FT – what does this mean in the broader landscape of telecoms triple play businesses? BT invested a lot buying EE and building up their media properties, Virgin Media still have a triple play offering and Sky has built up both broadband and an MVNO (paywall)
At the beginning of this month Amazon launched an addition to their Dash ordering hardware with the Amazon Dash button. There was a lot of incredulity amongst the media heightened by the unfortunate timing which overlapped with April’s Fool Day.
Why the incredulity?
I would break the cynicism down into two broad buckets:
The Amazon Dash button has a very singular usage / use case, narrower even the Yo! app which was a bit of a tech fad last year. Critics are at best uncertain that consumers would use them? I generally buy toilet rolls every 4-6 months, do I really need a button for that?
The Amazon Dash button implies that the hardware required is ridiculously cheap. How many boxes of washing powder, packets of Mac & Cheese or toilet rolls would be required for a button to break even?
Business perspective
Rather than ripping into this into too much depth I thought I would share Benedict Evans’ interesting hypothesis about the Amazon Dash button:
Amazon is trying to eliminate both vendor and brand decisions, and turning itself into a utility company – get your house connected to power, water, gas and Amazon. And choosing which commodity product you need is just another piece of friction to be removed by Amazon’s kaizen
There are some interesting directions that come out of this view point. Let’s break Benedict’s analysis down chunk-by-chunk:
Eliminating vendor decisions: there are two prongs to this. Firstly, it would reduce the basket size for supermarkets and also reduce impulse purchases. Let’s think about the Walmart ‘beer and diapers’ retail urban legend for a moment – if you weren’t shopping for the diapers, you aren’t likely to have picked up the beer next to it as you would have had no reason to go near those shelves. By implication it is also an attack on some of the categories carried in convenience stores. Given that the button is about ‘just-in-time’ shopping it implies that the users are not likely to have rooms in their lives for big box retailers or CostCo. The buttons are likely to aimed at urban dwellers rather than the suburbs were larger homes and larger vehicles to do the big box store shop are the norm – Sam’s Warehouse is safer than Walmart in this scenario
Eliminate brand decisions: since sales are diverted from supermarkets this also affects their private label sales, especially where they are acquired by accident as lookalikes stacked next to well-known brands. Challenger brands find that switching becomes much harder as they can’t intercept the customer at the point-of-intent through shopper marketing and the opportunity cost for the consumer gets raised due to the comparative nature of the friction in purchase. It also begs a question about how much it affects the share price of WPP and other marketing combines who have spent big on shopper marketing acquisitions over the past few years. Do buttons offer a net gain or loss of value to them? I do know that the button puts Amazon in a much more powerful position versus vendors in terms of discount pricing to retailer and warehousing. The key to understand the power that Amazon would bring is ‘choosing which commodity product you need…’. The very idea of a product being boiled down to a commodity buy would scare the living daylights of the average brand manager in an FMCG mega-corp
Turning itself into a utility: for Amazon this is about locking the consumer in via Prime to the consumer life. At the present time, logistics costs have been an increasing proportion of the cost of sales for Amazon, there must be a hope that the scale of grocery shopping will bring down the price of Prime and drive profits higher?
There is no reason why the likes of Tesco, Ocado or Iceland couldn’t have done this. The wider Dash technology would make it easier for consumers to do grocery shopping and reduce the friction of online purchases. Instead they seem to have wanted to reduce cashier numbers inshore and focused on self-service tills. Time will tell if they made the right technological choice.
What about the user?
This is designed to make the consumers life easier and I can see how it makes purchase of otherwise annoying to shop for items frictionless, but it only works within reason. You can’t have a wall of buttons on the front door of your fridge freezer and just when do you press the button in the bathroom to order up more razor blades or toilet roll? What happens during the run up to Christmas when Amazon has had sub-optimal performance with regards deliveries on occasion? What is the buying frequency required to make the button habit forming, used without thinking about it, without consideration. When does the opportunity cost for the consumer tip in their favour regarding button usage?
What I don’t have yet is a clear understanding on depth and breadth of the customer problem being solved by the Dash button.
Product design
The original Dash device was interesting because it represented a rejection of the broader theme of convergence where functionality is subsumed from dedicated hardware into a software layer running on a computer, via a web browser, tablet or smartphone. Instead Dash is a shopping appliance and wouldn’t look out of place in a cupboard full of Braun kit.
The Dash button represents a further evolution of specialist hardware, a brand-specific, tactile hardware interface. It mirrors software like IFTTT’s ‘Do’ application, the Yo! messenger app and the Dimple smartphone button project.
For non-food products like toilet rolls that come in a plastic bale that is quickly discarded, there may not be a barcode to scan in on your Dash device. Instead you would have to ask for a new pack of Charmin’ or more Mach3 razors. Processing each voice message is expensive, which makes the opportunity cost around creating dedicated buttons for certain classes of product much more attractive. Amazon first and foremost is a data-driven company, they will know which product categories that they want to have buttons for. However, what makes on an Excel spreadsheet doesn’t always make sense to the consumer…
At the end of last week Amazon unveiled Amazon Dash: an accessory to aid ordering from its Fresh grocery service. Fresh promises free same-day delivery on orders of over $35 of more than 500,000 Amazon items including fresh and local products; including products from respected restaurants and coffee shops. It has been rolled out in three major US markets: San Francisco, Seattle and Southern California.
Fresh has a mobile application on both Android and iOS to aid in shopping – which makes the launch of Dash much more curious. Dash is a piece of dedicated hardware which implies a failing in terms of ease-of-use for the smartphone application. Amazon obviously thinks that Fresh customers will be heavy high-touch, high-value consumers in order to spend this much trouble engineering and manufacturing the hardware and supporting services to make Dash work.
Dash is a product that wouldn’t be out of place in a collection of Braun kitchen appliances. It’s hardware interface so simple it looks really intuitive.
The Amazon Dash can be seen as part of a wider movement from converged general purpose devices to dedicated hardware. It is interesting to compare and contrast the Amazon Dash with the :CueCat; how just over a decade can make such a difference to a product. Back in 2000, Wired magazine sent out the :CueCat to US subscribers of their magazine. The :CueCat was a barcode scanner that allowed readers to augment the print content with a link to web content. Think a prehistoric QRCode. It didn’t work that well for a number of reasons. The codes were proprietary, partly due to consumer privacy requirements and intellectual property around barcodes. In order to use the :CueCat one needed to be connected to an internet-enabled PC via a wired USB or PS2 connection. Using the :CueCat was no easier than typing in a URL or searching via Google; a search engine on the ascendancy at the time. The :CueCat was a spectactular failing for the media industry looking to get to grips with digital media.
Moving forward to the Amazon Dash, the equivalent computing power of that desktop PC has been squeezed into a device that fits in the palm of your hand. Wireless connectivity provides a more flexible connection that removes contextual restrictions on the Dash compared to the :CueCat. The web extended computing so that the website and the PC or mobile device in a symbiotic relationship where it isn’t clear to consumers just were one starts and the other finishes.
The Dash takes inputs via a product barcode and voice memos. Despite the technology advances over the past ten years with the likes of Siri and S-Voice; there will likely be some sort of human intervention required to make these voice memos work. This is at odds with Amazon’s warehouse robot systems and lack of a human customer service face over a telephone line.
This voice memo challenge is not trivial, it was a contributing factor in SpinVox’s failure. The Fresh programme because of its logistical challenges will be hard to scale, and the economics of the Dash have to be carefully balanced between existing products that are repurchased via barcode scan and new or fresh products that would use the voice memo. Acquiring basket growth becomes incrementally more expensive. Over time the system may learn voice commands rather like Google’s old telephone-powered search; on the one hand local area focus is likely to limit dialect variations, on the other sample size maybe hard to scale to be statistically significant for machine learning. More related content here.
Les Binet did some sterling work thinking about share of search volume as part of his ongoing work looking at marketing effectiveness.
In order to understand share of search volume, we have to go back to 1990 when former advertising veteran and professor John Philip Jones[i] published a paper in the Harvard Business Review[ii] and a subsequent book[iii].
Jones’ research around the linkage between advertising and sales by looking at advertising including tools of his invention STAS (short term advertising strength)[iv] and AIC (advertising intensiveness curve). One of Jones’ key findings was the linkage between a brand’s share of voice and its market share. One of the biggest predictors of brand growth was ESoV (excess share of voice). ESoV is when a brand has a share of voice in excess of the proportion needed to maintain its market share.
During economic good times this might be down to an increase in brand building marketing spend, not only advertising and public relations, but also influencer and sports sponsorships with variable[v] results.
During recessionary times[vi], it might be maintaining brand building marketing spend when the competitors are cutting back.
Part of this brand building work overlaps with increasing marketing penetration through increasing the number of places where the brand is available. During the 95 percent of time that you are not in a buying mindset when you pass a product display in a supermarket it’s a billboard – doing the brand building work.
Jones’ findings were later validated by Peter Field and Les Binet’s work on marketing efficiency[vii], and in the summation of research[viii] from the Ehrenberg-Bass Institute for Marketing Science by Byron Sharp.
Share of search volume
The clever thing that Les Binet[ix] did with share of search volume[x] was find it as a predictor on the likely time when ESoV was likely to impact with a growth in market share AND, he found that the share of search volume change mapped neatly on to market share change.
The challenge is that different sectors have different times between a change in share of search volume and the corresponding change in market share[xi].
“For mobile phone handsets, Binet further ventured, share of search leads market share “by about six months” as a performance indicator – offering marketers a chance to adapt their strategies if needed if a decline is expected.
“If the brand … had access to the share of search data at the time, it would have had a six-month warning that share of market was about to turn around,” Binet said. “That’s an incredibly useful metric.”
Share of search’s predictive quality for energy brands, Binet explained, was noticeably shorter, at just “nought to three months.”
For automakers, by contrast, share of search anticipates market share by “nine to 12 months,” he said – a significant timeframe for marketers to potentially refine strategies.
Breaking out data for Volkswagen, the auto marque, provided corroboration that sales forecasts based on share of search “are incredibly close to what actually happened,” Binet said.”
Search considerations
Much earlier in my career I worked on the Yahoo! Search business, back when the company had its own search technology and sold its own search advertising. One of the things that we found was that while overall search volume could be modelled accurately for the year based just on January’s search data – unexplained search volume peaks still needed to be ironed out by looking at rolling three-month values instead.
I found it interesting that Binet’s findings didn’t seem the same degree of ‘peakiness’ and was a much more valuable predictor once the time lag factor between share of search volume and market effects were known.
Share of search makes sense from a logical perspective. Many below-the-line activities have been focused on search in terms of aiding SEO to increase share of market opportunity, rather than an explicit appreciation of the impact on the share of search volume and consequently change in market share. My friend James Warren used to talk about public relations and related earned media activities such as organic social media as ‘offline SEO’. This thinking was incorporated into Interpublic’s ‘inline’ concept[xii].
Future search
Share of search volume is complicated by a number of factors that are down to changing consumer behaviour.
Google’s focus on mobile upended the precision that we could search with and what we could search for, out went Boolean operators that could track down a highly relevant web page from 12 years ago. But we could now find the nearest coffee shop with wi-fi. YouTube[xiii] due to its explanatory content became the second largest search engine globally (excluding China).
A good deal of product search has migrated to sites like eBay, Walmart[xiv] and Amazon[xv]. Part of the reason being is that their site search is good enough, they have a wide range of stock and speedy delivery. Amazon also benefits from Amazon Prime which drives customer purchase, but isn’t without controversy[xvi].
Social and generative AI have unlocked new challengers to Google. Search on social platforms has become the go-to approach for many young people. Google acknowledged this when asked by Business Insider[xvii].
“we face robust competition from an array of sources, including general and specialized search engines, as well as dedicated apps.”
The move to social is about tapping what we called back in my Yahoo! days ‘knowledge search’[xviii]. Search startups like Gigabrain have tried to tap into this market by providing a better search function of Reddit forums.
Finally, the move towards consumer usage of generative AI tools based on large language models has created new competitors to Google including Perplexity and ChatGPT Search. Google itself has adopted LLMs in its own search offering and seen an increase in both revenue and profit from search advertising[xix].
Share of model vs. share of search volume
In order to try and understand new LLM-driven search, innovator agencies like Jellyfish and Deft[xx] have looked towards understanding share of model. Share of model tries to understand how LLMs perceive a given brand, in a similar way to the way SEO rankings held a similar place in search engine marketing. Like SEO, they look to understand whether the brand has sufficient optimisation of their digital properties to feature in recommendations by the models.
What share of model doesn’t give us is the consumer insight provided by share of search volume. Share of search volume is consumer behaviour driven and advertising influenced; share of model is algorithmic behaviour driven and training influenced.
