Alex Steer

328 words | ~2 min

Lots of agencies have processes for planning advertising. Often, they have extravagant adjectives attached to them. They are 'proprietary', 'unique', occasionally even 'groundbreaking'. They usually involve geometric shapes in unusual combinations, or carefully templated Word documents.

The distinctions between those processes are, as you'd expect, more or less meaningless. They typically involve understanding the challenges facing the business, the behaviours/needs/barriers of the users or audience, and the existing positioning of the brand in those people's minds, to arrive at some sort of imperative for the advertising. (Usual caveat: by advertising, these days, I mean any sort of branded communication or service.)

These reminders are useful, and they increase the chances of good work being done - but unique they are not, nor are any of them uniquely effective. This is because the most important differentiator in developing advertising isn't what the steps in the process are, but how they are carried out.

Small teams, working together, talking often, iterating every day, getting regular feedback, lending expertise, being honest, testing out ideas. The standards of collaboration we apply to each step. All the learning on effective delivery suggests that sometimes the best processes involve doing the same thing over and over in short fast cycles, together, getting it progressively more right.

But even more important than these are the things that happen between each step of whatever process we follow. The check-ins, the reviews and measurements that tell us whether we are on the right lines and whether the direction we're taking is worth pursuing. It's not easy to say 'no' to something you've all been working on, but sometimes it's vital. These standards of judgement stop us from going astray.

Together, standards of collaboration and judgement get work out the door faster, and make sure it's on track. They depend on co-operation, humility and generosity with ideas and time. That's a bit harder than pinning a Venn diagram above your desk, but a lot more rewarding.

108 words | ~1 min

Today I got a spam tweet from a company that does A/B testing. It read:

@alexsteer If you're interested in A/B testing, would you like to follow us?

I declined. Then I looked at their Twitter feed, and saw that they'd sent the same message to lots of other users.

Using the exact same words.

Surely, if you work in A/B testing, you mix it up a bit?

Branding consultants who talk in corporate speak. Social agencies who repost banal links. CRM companies who get your name wrong in the email. Weird how we drink our own kool-aid but don't eat our own dog food.

347 words | ~2 min

This post by Faris Yakob got me thinking with this side thought:

My concern is that the advertising industry is in danger of being taken for a ride by the same people who have destroyed trust in banking.

I think the ad tech industry is currently repeating a lot of the mistakes that the financial industry made a decade or so back. And look how well that played out for us all.

It's hiring a lot of people with backgrounds outside of marketing, with specialist skills in statistics and software, and setting them interesting challenges around measuring and optimising the performance of ads and media bids. All of which, when done properly, makes the industry smarter and better informed. The problem comes when those skills aren't balanced with a knowledge of what that industry is trying to achieve in the world. Then you get what you saw in finance and what you're seeing in advertising - solutions that trend towards the things developers can do most simply, and the things they find most interesting, rather than what's most important. And you get a lack of proper oversight and understanding from the people who commission and sell those solutions. The result is a lot of black box algorithms and snake-oil tech that neither the buyers nor the sellers can explain. And that creates a classic market for lemons.

Advertising needs smart planners and smart technologists working together: using strategic thinking to understand what ads really need to achieve, data analysis to see if they're achieving it, and technology to help them achieve it better. And even if the 'how' of the details is specialist, the 'what' shouldn't be. No part of the process of making, distributing or measuring advertising is so complicated that you can't explain it to a moderately well-informed person.

You should know what you're buying, basically - what it's meant do, and whether it does it. Anything else is not just pointless, it's risky.

285 words | ~1 min

If you work in technology, you know the Gartner Hype Cycle - their superb rundown of what's hot, what's busted, and what's valuable in the field of emerging technology.

They've just released the latest update, and here it is:

So how do I feel about the fact that what I do - big data and content analytics - is sliding down into the 'trough of disillusionment'?

I couldn't be happier.

The peak of the cycle is where investor money pours in, startups proliferate, sales pitches are made, forecasts are inflated, and lies are told.

But the trough - where people are let down, angry and disillusioned - is where businesses grow, and where money is made.

If you survive long enough to make it to the point where people are fed up hearing about your industry, you've probably learned a lot along the way. About how to put yourself in your client's shoes; about respect for existing ways of working; about the limitations of what you can do; about the importance of a good team; about what really makes a difference to the ways a business operates. If you're still around once the hype money has flowed out, there's a chance you'll make it.

My bet is that the next year will see the quiet extinction of a lot of marketing data/analytics startups that never found a business model. The ones that survive will be the ones that spent the last few years doing more listening than talking, and that are now answering useful questions that businesses have been asking since before big data was a thing.

522 words | ~3 min

We published this update to clients earlier this week. I'm reposting it here in case it's useful to anyone working with Twitter data. If you have questions about this or want to know more, drop me an email.

Within the last few days, Twitter has released a major update to its analytics platform. One of the new metrics included is 'impressions' – a count of how many times a tweet was seen. This information was previously available only for promoted (paid) tweets.

Fabric's data science team has been analysing the new data for our clients' accounts. The result is striking: on average, only 16% of followers see each tweet that a brand publishes.

Why this matters

The update means that, for the first time, brands can compare the organic performance of their tweets against their other media – and particularly against Facebook.

Facebook has been widely criticised for the declining 'organic' reach of brand posts, which we have been tracking since Q4 2012 and which is now as low as 2% of the total fan base, for large brand pages. Twitter has largely escaped such criticism because there has, until now, been no way of knowing how much free reach a tweet gets. This means it's been impossible to quantify the media value of a Twitter follower.

This has led to some fairly wild assumptions. A common industry shorthand for 'Twitter reach' is the total number of a brand's followers, plus the total followers of anyone who retweets that brand's content. This is a hugely optimistic measure of 'opportunities to see' that doesn't stand up to scrutiny: we know that people don't spend all day glued to Twitter, and they don't see every tweet from every account they follow.

The average reach of a tweet

For the last two years, Fabric's data science team has used an algorithm to give a rule-of- thumb estimate of true organic impressions. We've suspected that the view count of a tweet is a low percentage of the follower count, just as it is on Facebook.

Over the last few days we have been digging into the new Twitter data for our clients. On average, we've found that an un-promoted tweet is only seen by 16% of a brand's Twitter followers. For 'reply' tweets, the figure is typically between 1% and 2%.

Retweets help improve organic impressions, of course – but not by the vast amounts assumed in many calculations. There is no significant relationship between 'favourites' and the impressions a tweet gets.

Two metrics that matter

Marketers should pay attention to the new 'impressions' metric. It shows how far a tweet really travels and will let brands do better optimisation of the best times of day and week to schedule un-promoted tweets.

The other metric worth watching is the 'multiplier' on promoted posts: organic impressions as a percentage uplift on paid impressions. This is the hard currency of social media – the extra reach you achieve as a brand because people chose to follow your account, and retweet your content.

214 words | ~1 min

If you care about the question implied in the subject line, read Martin Weigel's speech to the APG, but especially this:

In a world characterized by constant change and innovation, planning will be knowledgeable about the fundamental principles of marketing and communications. It is breathtaking how little planning knows about how businesses actually make money, and how brands grow and are sustained. It is equally depressing how uninterested many planners appear to be in any of this today. Planners who find this stuff too tedious, or beneath them, would probably be better off advising production companies, than advising clients on how to address their business issues. In contrast, radical planning will take a keen interest in how our clients actually make money – in the business behind our clients’ brands.

There's so much wisdom in the whole piece, but I believe the root of it all is what's above. Planners should know how to use marketing communications to help increase an organisation's revenues and profits, by reinforcing and changing perceptions in ways that reduce the cost of sales or justify higher prices. If they can do that, they will still be valuable to people who manage brands, regardless of the kind of organisation they work in.

400 words | ~2 min

The more I think about the tendency to overstate the importance of ROI in digital and social media channels, the more I wonder whether marketing technology companies are implicitly trying to reshape their clients in their own image.

If you run a software start-up, you really care about sales funnels. That's because most of your marketing activity is sales activity. You develop a great product, you get feedback on that product and improve it, you generate qualified leads and you sell to them. With technology in particular, a lot of this activity happens online and can be tracked; new leads can be stored in CRM databases; and there is a defined customer acquisition funnel down which you can see those leads moving.

Do you know what doesn't work like that? Selling margarine.

Yet if you believed the way in which a lot of marketing tech firms talk about selling a £1.50 tub of margarine, though - or beer or jam or deodorant - you'd swear that it was exactly the same kind of problem as selling a £15 million software license. People who sell retail analytics software obviously want you to believe (and tend to believe themselves) that there's definitely a sales funnel in your category - you just need the technology to help you see it.

The truth is, there probably is no funnel if you sell margarine. You don't move from 'aware' to 'in-market' to 'loyalist' to 'repeat purchaser' in any meaningful way. In fact, there's excellent data demonstrating that in these categories loyalty (the classic bottom-of-the-funnel effect) is largely a function of market share.

So even if people did buy all their margarine online (and they don't), being able to track every stage in the customer journey wouldn't necessarily give you much advantage.

When you sell low-price products to the mass market, you grow share by making many small, weak, positive brand impressions in people's minds - not by 'closing' customers and moving them up a linear sales funnel. That logic works for high-price, high-consideration, infrequent purchases like cars, computers or mobile phones. For cheap, fast-moving goods it matters far more that you know which brand and advertising metrics correspond to sales growth, and that you have a way of measuring which advertising content and media is performing best against those metrics.

589 words | ~3 min

From time to time, people ask me how to demonstrate the ROI of social media. For the record, this is my answer.

1. You should calculate the ROI of social media in the same way as you calculate the ROI of other media.
2. You should calculate the ROI of social media content in the same way as you calculate the ROI of other advertising creative.

ROI is one of those trump cards that you can always play as a marketer or advertiser whenever you want to sound like you're being businesslike and focused. It gets played a lot when we're talking about digital or social because those things feel intrinsically less familiar to many advertisers than other types of media (TV, press or outdoor, for example).

As a result it can become a sort of comfort blanket, a way of saying 'I don't want to do this' that doesn't involve grappling with the difficult issues of how best to use new media channels to a brand's advantage.

'Show me the ROI' is an unfair question to ask, if you're not following the two guidelines above. Specifically, people tend to ask more of unfamiliar media than they do of familiar ones.

Do you know the financial contribution to your business of your last press ad, or of that sponsorship banner at the cricket pitch? Can you split out the contributions of the media (the placement and format of the advertisement) and the creative (the advertising content itself)? If you know those things, I am impressed, and you are perfectly entitled to ask the same questions of your Facebook posts or that witty Vine video you published.

If you don't, though, you should hold your social media and its creative content to the same standard of measurement as your other advertising - not lower, but not higher either.

That means, in practice, that where you have developed proxy metrics for one medium, you should also develop them for your others. Suppose, for instance, you have worked out that reach and frequency for your TV advertising is a valid indicator of future financial performance for your brand; you should apply the same modelling to your social media. If possible, you should do this properly, with your research agency, developing a valid model that will let you say, in future, that you are confident that metrics X, Y and Z are useful predictors of likely sales growth.

In lieu of that, it makes sense to start with metrics in one channel that you at least know are valid in another, if you think that there are good enough reasons to think that the two channels are broadly alike. If reach and frequency matter in TV, they might also matter in online video, for example; if in press, then perhaps in Facebook posts.

Doing that at least gives you a business case for continuing to invest in channels that you believe are important, while you test and prove whether or not they are. One of the weaknesses of social media providers is their enthusiasm for promoting their own rather obscure metrics, which don't allow for such easy comparison. One of the things we do at Fabric is to help marketers cut through that definitional clutter, to see which oddly-named social metrics in fact have more old-fashioned ones underpinning them: online equivalents of reach, frequency, impressions, word of mouth, etc., that at least stand a chance of being validated.

462 words | ~2 min

In July last year, at the height of the big data nonsense surrounding the doomed Omnicom/Publicis merger, I wrote this about the overused notion that 'it's what you do with the data that counts':

Everybody with a bit of common sense in marketing knows this, and could tell you where and when they want to use data more – when identifying specific challenges and opportunities, when taking the temperature of an issue and figuring out a response in real time, when measuring and adapting the performance of creative content mid-stream during a campaign, and when measuring the relationship between short-term activity and long-term brand value and behavioural change.

I think it's still true, and I think this is a crunch time for the firms that flooded the marketing industry selling the dream of big data. They're maturing, they need to get out from under the wing of their startup funders, and that means they need to start developing proper business models that attract customers and generate revenue.

Based on what I wrote above, I think there are four business models where big data will make a serious impact on the marketing industry. They are:

1. Adding good-quality behavioural data into the econometric models used occasionally in strategic planning.
2. Supplying metrics which can be used to measure and improve the ongoing performance of advertising content and media.
3. Supplying metrics as inputs to advertising effectiveness research and brand tracking.
4. Increasing the speed and breadth of information-gathering during crisis/reputation management situations.

None of these things - marketing strategy, media measurement, advertising research and crisis management - is new. Extra data and new technologies improve them rather than transforming them. Because of that, the data has to be additive: it needs to let marketers see or do what they saw or did previously, but better.

The winners are likely to be organisations who understand what data marketers already have, and how they use it: management consultancies, media agencies, and research agencies in particular. The most successful start-ups will target these relentlessly and concentrate on being useful rather than sounding smart. This is likely to be a bumpy transition for firms who have thrived on VC money rather than customer revenue for so long.

As the market matures, biggest red herrings will be the things that today sound the whizziest: ad-hoc predictive modelling, sentiment analysis, customer lifecycle modelling and automated adaptive advertising. All of these sound (and are) extremely smart, but they're a bad fit for what brand marketers want to be able to know and do, and how consumers behave in most categories. They make for a good sales pitch but a high-risk business model.

160 words | ~1 min

Time I got back into this blogging lark, isn't it?

That's going to happen both here, and at a Fabric product blog we'll be starting soon. It's been a busy few months, during which we've launched our product, brought on new some major new clients, and started nailing down our positioning, moving out of stealth mode and become a serious part of the marketing data landscape.

If you don't know Fabric or what we do: we help global brands use data to make the most of their digital content. Our main product is a web app that helps marketers see how their digital content is performing. We've been quiet for a long time while big data's been going through its hype phase, but we're now collecting a billion lines of data a day for 150 brands in 25 markets, so it's time to make a bit more noise.

In a polite British way, of course.