IN DEFENCE OF LAST TOUCH ATTRIBUTION (because I keep reading people slagging it off) Suppose you're a small business owner who has to deal with online sales. You have four hours a week max to check your website performance - usually much less. And you want to keep an eye on where your sales come from. So you start seeing where a user was just before buying, and attributing sales there. That's the last-touch attribution model, which gives 100% credit to the final marketing touchpoint before someone makes a purchase. But here's the challenge – this sort of tracking doesn't quite represent how we really shop. The customer journey is a labyrinth of touch points, each doing some work to 'win' the sale. Linking a sale to a specific activity is a huge oversimplification; just because someone bought from you just after seeing an ad, doesn't mean that ad was what drove the sale. A full team of touch points wins the game, not one player. Indeed, Nielsen did a huge study on attribution in 2011, 'Beyond Clicks and Impressions', and found the relationship between engagement, clicks, or even ad recall is pretty close to nil. But the dirty little secret is that no model is perfect. Data-driven, linear, time-decayed or other models don't *really* capture what happens. They might be more accurate, but they're never perfect – and if you don't have hundreds of thousands a year to spend on analytics, then you need a quick-and-dirty way to see where your sales are coming from. Even if it's not perfect. That's where last-touch shines. It cuts through the noise and focuses on what really matters: the final push that led to a conversion. It's attributing your goal to your centre forward, even if your midfield has done all the heavy lifting storming past the defence and setting up an easy shot. And last touch was used for over a hundred years – Claude Hopkins turned marketing to a science with couponing, a last-touch attribution model, with huge success. It's principles are still alive today, and worth tens of millions - Nectar loyalty cards was sold to Sainsbury's for £60m just a few years ago, after all. If you want wholly accurate modelling of your customer journey, and you've the budget to implement it, then last touch isn't right for you. But if you need to quickly see what platforms are where your customers are coming from, its bloody great.
💻 Steve Hawkins’ Post
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Deciding which channel creates the most impact in sales conversion aka Attribution Modelling In 2017, I used to work at a startup as a telecaller selling high end electric bicycles for Rs 35,000-45,000 (10 times the normal bicycle) After making at least 2,000 calls I finally sold 2 of them. I threw my laptop aside and broke into the CEO’s cabin to announce my achievement. I proudly announced about the 80,000 sale Great he exclaimed!! But your sale is not just yours he politely said The last touch point is not always the most important He said it wasn’t just you that generated the sale, it’s a mix our marketing efforts. Here’s a sample journey of a conversion 1) seen our insta advertisement and just clicked for curiosity 2) researched other cycles on Amazon and forgot it the next day 3) the insta add again popped up after 2 days 4) went to our website and checked details 5) inquired about other electric cycles, taken test rides from a few shops in the locality —At this stage the person is 90% convinced already 5) called you up to understand the after sale services and refund policies 6) converted So, your conversion was not just your conversion, the customer came from many different channels before coming to you How much exactly did I contribute to the sales process? But, how much contribution did I make in the sale, was it 5% or 15%? Determining this weightage is Attribution Modelling Determination of weights If you analyse the data of all the converted customers and which channels did they pass through, you can estimate the weightage of each channel. Perhaps by creating a linear regression model for a company advertising through Insta and google ads. Here’s what the model looks like:- y bar = b0 + b1x1 + b2x2 y bar = total sales b0 = sales without any advertisement x1 = advertisement expense on insta x2 = advertisement expense on google ads b1 = weightage for insta add b2 = weightage for google ads b1 and b2 can also be interpreted as the jump in sales by spending money on the respective advertisement channels Eg: if the model gives output saying b1=2 and b2=4, then google ads is twice as effective as insta add (assuming no multicollinearity between google ads and insta ads I.e they are not interrelated to each other) Application of model in decision making Once you decide determine the effectiveness of each channel, you can tweak your budget to maximise sales. In other words, if you want to generate $100 worth sales, how much to spent on each channel? This decision becomes fact based through attribution modelling.
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Had fun with this guest blog for Triple Whale! Total Impact attribution is honestly an incrementality hack. It might be the most simple way of creating a type of advertising "multipliers". Read the article for more details! And props to Olivia Meyers for the help with this one 🤝
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Do you find it challenging to pinpoint exactly what drives your customers to make a purchase? Our latest guest blog by Daniel Shepherd, Strategy Lead - Paid Social at adQuadrant, on Triple Whale uncovers groundbreaking insights into leveraging Total Impact Data and post-purchase surveys to drive your business growth. 🔍 What You’ll Learn: ➡ The power of combining pixel data with direct customer feedback for a complete view of the customer journey. ➡ How Triple Whale’s Total Impact Attribution Model fills gaps left by traditional analytics tools. ➡ Real-world examples of optimizing ad spend and enhancing product development through post-purchase surveys. Discover how to allocate your budget more effectively, fine-tune your marketing strategies, and achieve measurable growth. https://lnkd.in/gicZDHwK
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Ecommerce acquisition costs aren't just creeping up; they're sprinting, and it's causing more than a little stress for brands trying to keep up. The landscape is shifting under our feet, with shoppers changing their habits and privacy laws getting a whole lot stricter. That old playbook on marketing? Might as well use it to prop up a wobbly table. So, what's the game plan for brands looking to not just survive but thrive? 🔬 First, brands need a clear picture of why getting new customers is suddenly costing more. 🎯 Then, shake up marketing strategies, making sure spending is smart and targets are hit. The trick is to get savvier with the tools and tactics, seeking out those that give the clearest data and the best insights for smart decision-making. Here's what to focus on: ▪️ Finding ways to collect data that respect user privacy ▪️ Getting better at identifying which ads are working ▪️ Making sure they’re reaching the right people more effectively Polar Analytics has three tools tailor-made for these challenges: 1️⃣ Polar Pixel: A first-party pixel that dodges blockers, offering up the kind of data on attribution and campaign performance that you can actually trust. 2️⃣ Conversions API Enhancer: Feeds the more accurate Polar Pixel data back to your ad platforms to optimize their targeting algorithms (and in turn, increase spend efficiency). 3️⃣ Creative Studio: Sheds light on the effectiveness of your ad creatives, helping you fine-tune your approach. Want the inside scoop on how these tools can help you manage those rising costs? Check it out here: https://bit.ly/49BFCN2
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