This week we’re having a look at an early stage startup in Singapore. PrecisionBit is hoping to change the way that advertisers connect with their target markets with clever use of machine learning. They are really early stage though, so not a lot of information is available about them. However they are part of Telstra’s Muru-D incubator so they’ve got some funding and just as importantly people who believe in them. Let’s see what they are trying to do and how it works
The basic premise is that PrecisionBit scans through all the metadata that it can find on billions of images on the web and then uses that to recommend visual themes that will appeal to target audiences. In their own words
PrecisionBit analyzes information embedded in user-generated photos to
recommend visual themes that will generate the most audience interest
There are a number of ways of doing this but clearly the largest sources of user generated photos on the web are found on Pinterest, Facebook, Instagram and other social networks. There are plenty more sources of course but what is critical about these and similar sites is the association of user data with the images and signals about the power of the imagery.
Let me explain. Your presence on Facebook establishes you as part of a particular demographic. Age, sex, location etc all allow advertisers to focus in on you. Now I’m fairly sure that social network analysis shows strong patterns in the makeup of people’s networks. So for example a 42 year old may have a multigenerational family component, a hobby component, a life long friends component and a work component to their network. Broadly each of these components has a range but across large numbers of users we can make some useful simplifying assumptions. That say 92% of a 19 year old American female in Dallas friend’s will be in the same demographic as her.
Then we can pretty quickly see what she posts and what she likes. If she posts something that’s a signal that the imagery, text of meaning of the image is popular. likes or upvotes are a further signal of the image’s strength
So if we know what this hypothetical user likes, and millions of other people like, then we can start analysing the data to see what very particular demographics like
That’s the background. The problem that PrecisionBit is trying to solve is that advertising click through rates (CTR) are declining as audiences get saturated with digital advertising. That’s a problem for advertisers. They have to pay more or reach fewer people meaning a worse ROI. At the same time Google and Facebook take 54% of the global digital advertising spend.
So by being able to look at the data and predict that a particular creative will result in a much higher CTR, engagement, conversion rate or whatever other metric is being used will directly push through to advertisers bottom lines. That’s a pretty good proposition. How does the business model stack up?
We’ll now take a bit of a safari through the different parts of the business model, make some educated guesses, and see how it could work
PrecisionBit, I think the name comes from CNC drill bits doing very focused machining on high tech products – or perhaps a focus on bits of data with nanometer resolution, states that they are after 2 customers. Agencies and brands. We can pretty confidently say that in it’s current state PrecisionBit is not a tool for Mom and Pop advertisers. They haven’t got the digital or the creative skills to worried about the exact colour palette used and it’s impact on engagement. You need to be large to worry about that and having a large ad spend for something like this to move the needle. Remember Google’s blue period of analysis.
So we are looking at large corporates, FMCG and the agencies that serve them. Specifically within them we are looking at the people managing and designing digital campaigns and of course optimising them. That’s a relatively small population of tens of thousands of people who can be prioritised and you can target the most likely prospects fairly easily with lists of big advertisers. (That’s a bit old but there are plenty of industry ranking available)
An alternative customer approach would be to ignore the brands and agencies and develop the technology for integration into Facebook or Google’s ad technology. It’s a small advantage for one over the other.
So that bring’s us on to the value proposition. I don’t really have enough data to do a proper value proposition design here but a simple summary of what PrecisionBit is saying on their website is that their value proposition is the:
Ability to predict the most engaging visual content to maximise ROI
For a digital marketer that’s taking away a bunch of optimisation pain and offering her the chance of higher performing campaigns and better ROI. Of course it’s just another weapon in the arms race. Once the manager’s competitors have it the net effect will be to solidify market share and increase their defences against incumbents.
That said the VP is powerful and attractive and there should be the tech behind it to deliver. There are a number of alternate VP’s lurking in the background for other product lines. The tech for example could easily be used to be identifying negative pictures and thus as an anti-spam mechanism (that would require real time analysis)
As we’ve seen there’s probably a relatively small number of target customers. This is not a mass B2C offering. It’s also worthwhile considering that, depending on the price point, many of the users will be technical marketers rather the deciders who will be targeted by marketing and sales. The aim will be to provide technical marketers with another tool to improve their conversion rates. So pretty much I’d expect this to be a standard web based automated tool that spits out answers with a support forum and dedicated helpline for higher pricing tiers.
At the moment PrecisionBit has a ‘contact us for demo’ form and negligible SEO and social media presence. It’s early days but it means that we have to take some guesses here. The marketing and sales team is weak except for the CEO, Rachelle Lao, who has a FMCG marketing background and a track record as a hustler to put big deals together.
That suggests that the primary route to market is going to be a direct sales route into large corporations using an existing network of contacts. Once that sales process is established then the primary channel is likely to be a traditional enterprise software sales route. This really does depend on the price point that PrecisionBit can achieve. If the company is looking at $100/month subscription then it will have to fall back on and inside sales team. If it’s selling it on a seat basis then the economics are quite different.
That really depends on the economic value that can be delivered to customers and how much of that PrecisionBit can secure for itself. .
It’s Saas. There are a bunch of ways of doing this. It will probably be a company or per seat subscription basis with several tiers. Pay for performance is probably too difficult to do as the campaign data probably won’t be flowing through PrecisionBit servers. A % of ad spend could be one route but this would be difficult and if CTR’s continue their decline this would either be complex of unattractive. So subscripotion sems a fair bet.
There are two key activities here. The first is to gather the data required. The second is to analyse it and use it to predict outcomes
I said earlier that there is loads of data inside Facebook – but Facebook is not famously friendly to releasing it’s data. It’s how it makes it money after all. So however good the API’s are they are not going to release the really valuable data. On the other hand if it is a walled garden it’s quite hard to climb over the wall. FaceBook has reasonably good anti scraping defences as do many other social networks. The less good a social network the easier it’s data is to access but then it is smaller, less representative and there is more statistical jiggery pokery involved to get statistical significance in the predictions. One alternative here is that the whole company is being designed for aquisition and everything that is being done is to demonstrate proof of concept by FB or Google. That would provide the raw data that would really make the proposition effective.
On the other side analysing the data is an easier problem. using photo metadata and social signals we already have relatively structured data and there are plenty of techniques out there for creating data sets and establishing predictions. The obvious question is how significantly does the image choice impact the campaign outcome, and lesserly, which of the key parameters have the most significant impact. Then you have to set that against the ability of designers and digital marketers.
My aesthetic talents are notoriously poor so even at a very early stage the PrecisionBit algorithm would be beating me. It doesn’t need to play at the same level as Lee-Seedol either. It does need to deliver a significant advantage to people who have been making creatives for decades, and have two centuries of creative history behind them and two millennia of art history behind that.
On a speed basis the algorithm would have little advantage. It and an experienced Creative Director would make a judgement in seconds. Creative Directors do get it wrong, especially in novel turbulent markets, so one advantage would be in the certainty of prediction. Does the algo get it right more consistently?
The other question and one which I have no answer is how does the machine learning play? By implication it should be looking at campaign as well as raw data and using that to refine it’s predictions. The more advertisers using the platform for moe campaigns the better the predictions would be but that would mean an aggregation of data at some level, even when anonymised, and that would lead to the transference of power between advertisers. In contrast running a different instance for each advertiser would lead to slower training and worse results.
The key resources have to be the algorithm and the data aquisition mechanisms. As mentioned in the Key Activities section the way that data aggregation or sharing happens could also be a key resource, as could access to detailed data from inside a Social Network fence.
Any Social Networks that come on board are going to be key partners as would be agencies with multiple large accounts which could act as resellers.
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