Archive for the ‘Data’ Category

Kpi

In order to prioritise effectively you need both the projected value and effort, but these aren’t always easy to come by. Projecting value can be particularly challenging if the data isn’t easily accessible which can have a knock on effect when analysing your KPIs (Key Performance Indicators).

Ensuring that a product / feature have KPIs is beneficial for a few reasons including: Aiding prioritisation, celebrating success, feeding back on software development iterations and to feed into the general product vision and wider business goals.

Your KPIs don’t have to be a financial value (although a good attempt at projecting a monetary value should be made to aid ROI projections) or just one KPI, but they just need to be measurable, an indication of success and for them to be linked in someway to the overall business goals, so how can you identify what your KPIs are:

  • Incremental revenue – benchmarking on existing revenue volumes for the relevant feature in question. What do you anticipate increasing the revenue / ARPU by
  • How many customer queries are you hoping to reduce and how much does it cost per contact
  • Is it solving a common problem / request that high value players have been submitting
  • Will solving the problem increase website stability, reducing downtime for customers
  • Are you expecting to increase customer acquisition numbers / conversion rate
  • Will it increase retention rates – a measure of this is churn rate / drop off as well as LTV
  • Efficiency savings – by completing a piece of work could it increase team output / Velocity whether it be development or a marketing team
  • Feature traffic / usage – if conversions or direct revenue from the feature isn’t relevant then at a minimum having sessions, dwell time and value of customers using the feature can be used as a KPI

    Identifying your KPIs is one thing, but having the data available at your disposal on a self-service basis to cut, analyse and share is naturally fundamental, but once you have identified your KPIs and have access to the data, you can be confident that you’re well equipped to contribute to the Agile piece, but also your helping meet the wider business goals.

    Problem solving image

    There will never be a lack of problems to solve when it comes to product development, but handling the relentless amount of ideas and identifying the right ones to focus on can be tricky if a robust process to prioritise and track all of these is not in place.

    There are many approaches you can take to handle problems and prioritisation, some product management videos I’ve seen describe the product manager to spend most of their time saying ‘NO’ to stakeholders which I tend to disagree with, because technically all problems can be solved and an idea is never a bad idea, but perhaps it just can’t be solved right now due to higher priority work, so a response to show appreciation for raising the idea / problem and that it’ll go through the prioritisation process is a more appropriate response. Some also handle prioritisation and requests by who shouts and flaps the loudest which is equally not the way to go about effective backlog prioritisation.

    Two things you need in order to prioritise effectively 1. Value and 2. Effort to work out the projected ROI. Before you even spend effort discussing how much effort a problem will take to solve, the first thing which should be asked when someone approaches you with a problem, idea or bug is “what is the value?”. As a result of this, you may get:

    • A sheepish response where they don’t know, so they’ll have to go and find out (in some cases resulting in the problem being so small it’s not worth solving it, so you don’t get to hear about the problem anymore)
    • Value provided is minimal (relative to everything else in the backlog)
    • A fluffy response eg. It’ll increase traffic, it’ll increase retention rates etc, with no given metric
    • A significant problem / idea to solve which could deliver vast amounts of incremental revenue, benefits to customers or savings through efficiencies which should be fast tracked through the effort sizing and prioritisation process

    Another question which can be asked to identify value is “what happens if we don’t do it for 3 months, 6 months or never” which will aid prioritisation further.

    At your disposal you should have a data visualisation tool to easily view trend data and access KPIs for your products and features which is another way of identifying value for yourself, but ensuring that you and your Scrum teams are working to solve the highest value problems is fundamental in achieving a healthy ROI and successful product, so by asking just some simple questions up front will make your journey a lot more palatable.

    So many fantastic ideas from so many people to improve the product, but it’ll always be impossible to fulfil all desires in an acceptable time frame to stakeholders, making prioritisation not only challenging but extremely important.

    Communication, data, collaboration and determination can certainly make prioritisation all the more effective and smoother, so looking at these areas in more detail:

    Communication: Status of iteration, where do product requests/bugs sit in the pecking order, ETA on delivery, investment cost and the projected value of iterations held in a transparent way for stakeholders to pull the data when they want will help with the communication overhead and help maintain trust.

    Data: To ensure that high-value items are being worked on you need data to back up assumptions. It can be easy to flap and try to make a problem out to be bigger than it is to get it done, but there should always be some kind of data to back it up with examples being: incremental revenue which can be reverse-engineered from retention uplift rates or projected acquisition volume increases using LTV for example. Other ways of projecting value / determining scale of the problem are customer support queries or customer feedback, site loading times, efficiency in terms of £££ saving eg. Manhours/days or software costs etc.

    Collaboration: Discussing value and priority options openly with your colleagues will help you deliver a product in a more confident and focused way, as it’s not easy making the big decisions on prioritisation because what’s at the top or moves to the top means that the items below won’t be done now or perhaps anytime soon, so checking and agreeing on the focus/roadmap helps to give confidence to just get on with delivering a high quality & valuable product without having to worry about justifying a decision you’ve made alone every minute of the day. The final decision of course lands with the Product Manager who is accountable for the success of the product and prioritisation decisions, but getting feedback from stakeholders/colleagues in an inclusive way can yield even more positive outcomes.

    Determination: Prioritisation changes frequently if you work in an agile environment, so being positive and determined to deliver upcoming high priority / high effort iterations you’ve been discussing for months or even years helps to keep the focus on delivering the key product goals and provides reminders that it’s still on the agenda, no matter the level of incoming bombshells/distractions.

    If someone asks for something to be done urgently without providing any numbers representing the projected value or any element to give an idea of the scale of the problem you’re looking to solve, then asking why do it or what happens if we don’t do it in the next 12 months should help to quickly prompt the need to research more into the value.

    Projecting investment cost and taking time to dig into the projected value the product iteration will make collaboratively, will ensure that you’re delivering frequent value to customers internally and externally in a happy, fun and relaxed environment.

    image

    I had the pleasure of catching up with Paul Silver who is Chief Strategy Officer at Media IQ the other month. From planning and buying across multiple accounts to diving into data to really help solve client problems and needs was part of the discussion.

    Having an extremely granular optimisation and well executed programmatic strategy helps identify those high performing segments, but it also highlights a challenge when it comes to predicting / forecasting the ROI for the individual segments / campaigns. Each segment is likely to have significantly different ARPUs and churn rates and you want to avoid pausing a campaign which is performing better than the channel average, but also stop campaigns which perform worse than the channel average (LTV is normally worked out on a channel basis).

    Media IQ have built predictive and forecasting models to help advertisers solve this problem plugging these models into campaigns run by Media IQ or just purchasing them off the shelf to use in-house. The models which update as campaigns mature would give optimisers insight into which segments are most likely going to yield a positive ROI for segments without having to do manual calculations each time across a huge set of ad campaigns / segments.

    With £billions being spent on advertising there’s also £billions wasted, so it’s good to get some scientific help to avoid this as much as possible but also ramp up ad spend in the most attractive areas.

    This is a good example of how the new age ad agencies / consultancies are helping advertisers solve their problems without just taking media spend and adding a high margin on top.

    You can of course build your own models but if you want to avoid the hassle then I’d recommend speaking to Media IQ.

    It’s powerful, flexible, customisable, saves thousands of man hours, provides valuable customer insights / behaviour and most importantly ensures that you get a healthy ROI if used in the right way.

    Meet The Brain: The Brain is MediaMath’s proprietary algorithm and ingests data (60 billion opportunities everyday to be exact) and decisions against that data.

    Their algorithm’s left-brain and right-brain work together to analyse large pools of impressions, looking at dozens of user and media variables, to determine which impressions will best meet an advertiser’s goal.The Brain values each impression based on its likelihood of driving an action, and bids accordingly.

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    wpid-Big-window-in-Gothenburg-Version-2.jpg

    It continues to disappoint me when I hear about large blue chip clients working on the default 30 day PV (post view) cookie window for display campaigns and then accepting 100% of the PV conversions. Not only this, but in most cases no viewability tech is being used.

    When looking at your PV cookie window, typically it should be set to mirror what you have deemed to be the average consideration time to purchase as well as taking into account the ad format.

    On the other hand, you want to avoid coming up with an arbitrary PV window which so many brands do.

    Fortunately there is a robust way of finding out what percentage of PV conversions are genuine which you can use for future campaigns. This is called a ‘Placebo Test’. You would run an A/B test with one of your creatives adserved alongside a charity creative. Post campaign you minus the in view PV conversions which the charity creative delivered (which are obviously incorrect) from the in view PV conversions your brand creative delivered. This will leave you with the remainder of in view PV conversions which you can class as genuine. Work out what the percentage of genuine in view PV conversions were and then you can use this percentage within the buying platform which will mean only the percentage which has been proved genuine in the past will be accepted and attributed for the current and future campaigns.

    Ideally you should expect the ‘Placebo Test’ to look something like the below. If both lines are similar then the banners are not working on a brand basis and they therefore don’t offer any value outside the click. The mention of ‘Placebo’ below would be a charity creative.

    PV

    Things to consider:

    1. You need £10k media investment
    2. Banners incl. charity banners
    3. Partner eg. MediaMath, DBM or Media IQ
    4. Viewability tech eg. Spider.io
    5. You only have to run it once per product

    By overvaluing a channel like display has two main consequences 1. Wasting marketing budget as you could re-allocate some of the display budget to other better performing channels and 2. An algorithm optimising on bad data will only mean that it will aim to optimise towards that bad data more.

    On the subject of display wastage, I recently worked with Exchange Wire on an article about my frustrations of DSP’s not integrating with third party viewability tech and the impact.

    If agencies and brands stop wasting marketing budget and run display campaigns as they should be done in a more genuine way, the channel will then get the respect it deserves.

    computer-thief

    Can we place a pixel across your whole site and we’ll give you free customer insights? Can we place a pixel on each stage of the user journey so that we can optimise towards all site traffic data?

    These are two very common questions which originated from traditional ad networks and still lives on even though technology has evolved.

    If you ask a marketer if they could target anyone in the world with advertising with no restrictions, it would no doubt be their competitors customers.

    I am fortunate enough to have bought display remarketing campaigns targeting competitor customers in the past. This was when I worked across the largest hotel chain in the UK at an ad agency via an ad network. That level of targeting, special offer creative and high frequency reaped rewards as you’d expect.

    Marketers spend £millions a year on advertising and driving quality traffic can be expensive, so the last thing they want is a competitor just simply remarketing all of their users who visit their site either through FBX or display.

    Fortunately this can be avoided if marketing deploys a strict policy that they only allow media pixels to fire on an attributed basis, yes some partners might say that they’d need all data to optimise but when you weigh up value vs. risk, it’s simply not worth it. Optimising on attributed traffic only is good enough for third party ad partners.

    On the analysis front eg. Google Analytics, Click Tale, Quantcast etc. it’s a case of applying a bit of logic, experience and research so then when deploying tracking / pixels on site, your data will not be sold in a data exchange or given to a competitor for remarketing. When it comes to big blue chip companies like Facebook, Adobe and Google, there’s no need to hesitate about data security because if it gets out that they’re selling your data then it would be disastrous for them. Whereas the likes of Quantcast who are very well known for giving you FREE customer insights just for placing a pixel across your whole site, is one of those cases where big red warning lights should appear because in this world nothing is really for free and the likes of Qantcast make money from using your data.

    Having a strict cookie / tracking policy is safe and advisable but by not having one could cause your market share to decrease as your competitors steal your customers.

    You don’t walk across a busy road without looking in either direction so think twice before implementing code on your site.

    Listen

    You have a eureka moment and you start building out the infrastructure for product, website and staff.

    The product flies off the shelves and life is good. Customers are happy, customers are spending on your site and you’re living the dream.

    As the business expands it produces more risk to ‘issues’ across key business areas which effect customers such as product stability, CRM / customer service and product development especially when linked to different platforms. As time goes on, competitors pop up trying to attack your weak points offering a viable alternative and the ideal solution would be to have had long term business strategies in place to cover all areas which could be at risk of those ‘issues’ appearing, resulting in customers not having a good reason to go anywhere else.

    Many businesses only have short term strategies which apply temporary fixes and patches resulting in those ‘issues’ appearing in full view to customers.

    The 21st century has brought customer opinions and voices which are not only expressed across the globe but also across all channels especially social media and forums instantly within seconds to millions, so no longer does a customer have to write a letter to complain or stay on the phone for hours, customers are now in the driving seat not the brand.

    Those businesses who closely monitor, analyse and engage with their customer feedback especially their high value customers will avoid getting annihilated, emmbaresed and shown up in front of millions as well as having to pay high acquisition costs to convince new customers that they have changed.

    Brands need to stop thinking that they know better and start believing the classic saying that ‘the customer is always right’. Yes, not every single customer is right and you don’t need to add every piece of feedback to the business agenda, but apply logic to constructive feedback and where a clear trend appears apply it to the relevant business area.

    Not only are your high value customers willing to give you an abundance of ideas on how to improve, but you only have to give a tiny gift away to get vital feedback to improve business, which in turn once implemented will give you an abundance of organic new customers compared to the expense of having to use ad budget to acquire those new customers.

    On a similar note, it’s also not acceptable to put a product on the shelf when based on customer feedback post launch is clealy unfinished. A brand should never be in this situation, especially with so many tools available which would give you the feedback you need in order to build the ultimate product which meets demand prior to launch.

    So when you’re lying on the beach seeing the cash flow, you need to remember that if you don’t listen and look after your customers, you can lose them significantly quicker and in greater volume than you can aquire new ones and that is certainly not what investors like to see.

    NRC_RTB_V1-2

    Neil’s Recruitment have recently posted a fantastic RTB resource guide for grads, 1st / 2nd jobbers and those who are keen to know more about what RTB is along with all those other 3 letter acronyms.

    The RTB guide which can be accessed here includes:

    • What is RTB
    • Free training webinar
    • Blogs and trade press
    • Things to research/understand
    • Excel tips
    • Maths practice
    • Glossary

    It’s good to see recruitment companies like this going the extra mile to educate those who are new to digital advertising, which also clearly shows they themselves have a deep understanding on the subject.

    Cookie

    With ad spend still over £15bn / year in the UK, there are a few digital suppliers and publishers who continue looking for the quick buck by cookie stuffing.

    Worryingly some marketing consultants and CMO’s turn a blind eye or use the dodgy practices knowingly to improve on the marketing tracked performance.

    A few examples of cookie stuffing:

    • When managed service media buys are told to only run prospecting campaigns, yet they use remarketing aggressively to get the last post view conversion.
    • Suppliers popping banners across the net on a blank page to get the last post view conversion.
    • Publishers delivering multi banners below the footer of a site to get the last post view conversion and generate more revenue for themselves.
    • Ad networks requesting a click tracker for a piece of copy and logo, but then just use the click command to pop the site to post click cookie bomb.
    • Pop suppliers popping site when people search for your brand on Google – dropping a cookie when someone is just about to visit your site already.
    • Pop suppliers popping site using a click tracker and therefore dropping a post click cookie on the view – another form of cookie bombing.
    • Affiliates have an abundance of click trackers at their disposal where CTR doesn’t get monitored. Many use these to pop the site to post click cookie bomb also.

    These are just a few of the common practises which go on, but this neither helps the industry improve, is fair for genuine suppliers who do things by the book or helps advertisers grow volume incrementally.

    Fortunately there are a few tech suppliers out there who can at least help you identify whether traffic is showing a fraudulent pattern such as Traffic Cake.

    Agencies and marketing managers need to have a stricter policy on cookie stuffing so then it can finally be put to bed along with the suppliers who do it.

    In house

    Ad agencies have offered huge value for advertisers for decades and continue to do so. This will never change.

    The key benefits of outsourcing the media planning and buying function to ad agencies include the likes of: global negotiating power, specialist contacts for sponsorship deals, cross client learnings, cross channel integration, deal with the hassle and admin and it’s someone for the CMO or CEO to blame if the business isn’t hitting key targets.

    With programmatic buying (Paid Search, Social Media, Display RTB, Online Video, Mobile and Affiliates) becoming the bulk of digital Marketing, the majority of these benefits no longer applies therefore it doesn’t make sense and can be classed as lazy if a brand wasn’t to even consider taking all programmatic buying in-house.

    Although rightfully CEO’s obsess about growth, also ‘wastage’ and efficiency across the business needs to be reviewed often.

    Let’s look at the pros and cons for taking all programmatic buying in-house:
    Pros

    • New digital media team would be sitting next to all other marketing areas eg. CRM, creative, content, web design, product managers.
    • Close to business KPI’s and budgets so they can be extremely reactive.
    • No hidden margins in bid platforms.
    • Can often get cheaper adserving and bid platform rates.
    • Team become specialists in the business sector / vertical.
    • 100% of time and focus will be given to the one client.
    • Learnings and data won’t be shared with other clients with no chance of leakage to competitors.
    • Can turn around new campaigns significantly quicker.
    • 24 hour contact.
    • Will always have the time to stay ahead of the game.
    • Work closely with the data team / in-house DMP optimising on real KPI’s such as revenue / LTV / ARPU.
    • Can openly recommend business requirements to CEO in order to get things done quickly and grow the business.
    • No hidden agendas – everyone aiming for the same goal.
    • Other internal departments can be educated about what role the media buy has on business goals.

    Cons

    • Can take six months to recruit team, train grads and setup systems and data integrations.
    • Ad agency would have to make more effort to integrate offlline and online brand with internal programmatic team.
    • The CEO might ignore team recommendations of key requirements needed to improve marketing.
    • CMO would need to find someone who has +5 years experience in programmatic buying across all channels to head up the team and train the grads.

    There are certainly plenty of pros and if you’re wondering how to kick things off, speak to some of the recruitment agencies below who will be able to provide an abundance off free advice:

    Neil’ Recruitment

    Digital Bubble

    Cookies

    Firstly you need to have access to a DSP and have adserver container tags across your whole site. When implementing the container tags, it’s essential to pass back as much data through the custom variables as possible eg. age, gender, bucket amount, revenue, customer loyality type.

    Container tags should be placed across each site / product page and then a tag from homepage throughout the whole conversion process to the sales thank you page. Also a tag across the current customer login section is required.

    Now it’s a case of building up your CRM database within the DSP. A pixel within the DSP represents a targeting segment to be included / excluded such as:

    • Main acquisition homepage
    • March 2013 microsite landing page
    • Car Insurance homepage
    • Home Insurance homepage
    • Quote confirmation page
    • Logged in
    • Deposit confirmation page
    • Business awards landing page
    • Affiliate landing page
    • CRM email – non converting
    • Males
    • Age 25-34
    • Gold customers

    Once your pixels have been created in the DSP, it’s a case of implementing them within the adserver container tags using the existing variables which have already been setup. This will allow you to setup basic scripts to conditionally fire the pixels to match the segment. To increase cookie volume, implement separate pixels across all of your CRM emails also.

    The tech part is out of the way and now you just need to setup all of the relevant strategies in the DSP including / excluding the newly created CRM segments accordingly.

    As new product pages, websites, microsites and CRM email campaigns get created, then the same process needs to take place in order to keep the cookie CRM database updated.

    As the cookie database is held within a DSP such as MediaMath, you can deliver the CRM campaigns across ad exchanges, yield optimisers and FBX.

    Winner

    You’ve spent months working with the data team setting up all of the marketing data feeds for the DMP and now it’s a case of setting the briefs for multi and custom attribution models.

    Last click attribution is typically default and the most common. It’s not wrong to only stick to one and if there’s no motivation to work with others, then last click isn’t a bad choice to stick to.

    Viewing multi custom attribution models gives you insight into the campaigns which are getting undervalued by contributing more higher up the funnel than lower for example. Off the back of the data, you can then increase targets / goals / CPA accordingly for the relevent campaigns / media buys.

    The benefit of using custom attribution models is that you can amend certain exposures / campaigns in order for the output to make more sense in an actionable way eg. remove all banner impressions which did not get viewed, remove brand search clicks, remove remarketing impressions etc.

    Firstly the data team will need to setup the 5 key out of the box attribution models:

      • Last interaction
      • Linear
      • First interaction
      • Time decay
      • Position based

      Once built out, within your visulisation tool there should be options to customise the data further eg. remove banners which weren’t in view, remove brand search, remarketing and CRM campaigns which will leave you with insight into the real performance of your prospecting campaigns across different attribution models.

      Google have been attempting attribution modelling over the past few years via DFA. They unfortunately still have a couple of bugs making the tool unusable, but they are still further ahead than any other third party attempting custom attribution modelling on a self sevice basis.

      It will always be difficult for third party companies to successfully deal with attribution because attribution models should be built using the data from the in house DMP, which includes back end customer / revenue / LTV data.

      In order to understand how all of your ad campaigns are really performing and what role they fully play, viewing performance data across multi custom attribution models is key.

      Puddle

      Offline brand activity has been measured in the same way for decades through econometrics – mainly looking at the correlation which offline activity has with brand search volume and bottom line acquisitions / revenue.

      Many digital specialists claim that this way of measuring offline brand activity was built for offline and it would be unfair to use this method for measuring online brand. Yet, those digital specialists are more than happy to attribute post view data to all online advertising without analysing actual cause and causality.

      The reason why many feel that it’s unfair, is because online branding is expensive and when looking at the correlation of online brand spend vs. offline spend through an econometrics model, offline shows a greater ROI for many advertisers. Also when it comes to banners, in many instances there is zero correlation between banner impression volume and brand search uplift / bottom line acquisitions.

      Just because you can track post view, it doesn’t mean that you should attribute post view conversions to campaigns. Most digital planners who have been around for a while know how this can be easily abused, you only have to look back at the classic Yahoo Marketplace placement on the Yahoo HP where an impression counter could be attached to the ad to remember this.

      The key objective for all brand activity is to deliver a positive ROI no matter how the consumer got to your site / store or whether the ad was delivered online or offline. I can’t imagine any marketer spending money on advertising and not ever wanting a return from that spend, so it’s pretty safe to say that the key objective above is fact.

      So what is the most robust way of measuring the ROI of online brand activity.

      Analysing the correlation that both uplift in brand search volume and bottom line acquisitions / revenue has on any medium to large weight brand campaign (online or offline), is the most effective way of viewing impact / ROI in a robust and truthful way. This would mean that econometrics would be perfect to measure the effectiveness of online brand campaigns also.

      In order to determine cause / causality, the brand activity would have to be signficant eg. Portal / social network takeovers, online video or high volume display burst campaigns so then the noise will show up in an econometrics model.

      For very low volume online branding, there is an option to use in view post view data as a proxy of success, but it’s essential to remember that you won’t know whether the conversions would have happened anyway, unless you have run a placebo controlled test.

      The ultimate goal is to know what brand opportunities are the most cost efficient way of increasing conversions / revenue. Basic econometrics is still the most effective way of reaching this goal across all marketing channels.

      DMP’s 2.0

      Posted: Jul 21, 2013 in Business, Data, Marketing
      Tags: , , ,

      Puzzle

      DMP’s have been around for decades but the acronym only started getting banded around the ad industry recently.

      DMP’s up until recently pretty much included only back end data which was overlayed with a visualisation tool such as QlikView or Omniscope. Typically media planner buyers and marketing execs used to use adservers to pull off basic performence reports as all costs were flat ie. Not biddable and held within the adserver.

      Since programmatic buying became more popular, media buyers have been spending a significant amount of time pulling data together from different sources just to see how campaigns are performing – combining bid tool, adserver and back end data manually.

      Programmatic media buyers should be spending as much time as possible setting up strategies and optimising campaigns rather than spending days merging data or reconciling costs.

      Clearly things needed to change and they have started to, resulting in programmatic buyers having to work closer than ever to the database team who manages the DMP.

      Due to this change, the volume of work load and briefs to deal with data has tripled over night for data teams. To deal with the new data demand from marketing, it’s essential to have incremental resource to deal with the additional work because otherwise it will either take years to get done or get done in a shoddy way.

      Allowing marketing the extra data resource to support a data led marketing strategy is essential for business success. A DMP should now include log level data updated in real time / within three hours as standard including:

      • Back end data showing cohort conversion and revenue data
      • Paid Search bid tool spend and impression / click data
      • Social Media bid tool and fan page spend and impression / click data
      • Display bid tool spend and impression / click data
      • Banner inview data
      • CRM email impression / click data
      • Affiliate spend and impression / click data
      • Natural Search impression / click data with any flat agency fee attached
      • Mobile spend and click data
      • TV spots and any other offline channel activity with the relevant spend and volumes attached
      • Adserver data incl. adserving fee making all channel spends fully loaded
      • Site traffic data
      • Weather data
      • Competitor exposure data
      • Site / product issue data

      All of this data is essential for knowing exactly what is happening across the business and why. With a click of a button marketing should be able to view real time campaign performance (CPA and projected ROI) across all campaigns and channels as well as impact of what branding, weather, competitor activity and any site / product downtime has on revenue / acquisitions. Also user journey analysis from first touch point to last and the key five attribution models should be built out from the data which all take into account CRM.

      Without this, marketing cannot be expected to grow the business profitably.

      wpid-20130716_3.jpg

      The industry seems to be up in arms over the fact that mobile app tracking is not cookie based.

      Luckily where there is a technical issue, there is a simple solution and it’s extremely impressive how new mobile tech start ups have responded with clear and straight forward solutions which work. Has Offers have particularly impressed with their MAT (Mobile App Tracking) product which does what it says on the tin and offers fantastic client service – they do need a European office which is coming though.

      Like IP and cookie data, mobile tracking isn’t 100% robust but it certainly does the trick across all devices. Once you’ve chosen your mobile app tracking partner it’s a case of your dev team implementing the SDK and key events across the app passing back variables (especially across registration and sales sections) where applicable.

      Next step is to setup a new data feed taking the log level mobile data into your DMP which would have all variables and every mobile device ID you’d ever need to connect the front end data to your back end. Rather than cookie ID, what connects mobile data from click to install as well as from install to your back end data is the device IDs which fortunately for the industry rarely changes (only when you change phones or mess around with the OS).

      All mobile tracking tech comes with server to server url conversion tracking in the UI which is fully integrated into hundreds of mobile ad networks already, so you only have to make a couple of clicks and go live with a campaign with the mobile ad supplier for the supplier to view conversion / event data their end to be used for optimisation purposes.

      Mobile tracking tech is compatible with all digital channels including paid search enhanced campaigns. As you cannot third party post click track Facebook mobile install ads, you will need to rely on the mobile tracking tech to have an existing integration with Facebook, which would then pull in all of your Facebook campaign data into the mobile tracking tech UI.

      Mobile tracking tech are pretty much post click tracking adservers and similar to a traditional adserver you can set CPI / CPX / CPC costs in the UI, set cookie windows and set whether certain event and partner data is de-duped.

      Yes, the data team will be saying “not another data feed” but it’s a case of everyone cracking on and getting the job done.

      truth_and_lies_t

      Acquiring customers through brand paid search is in most cases not only the most cost efficient way of acquiring customers, but it’s also where most brands find where their most valuable customers originate from.

      As Facebook and Twitter release more ad opportunities by the week which are meeting advertiser demands and paid search CPCs increase especially across mobile, SEM specialists are finding it increasingly difficult to add value or are just simply missing the limelight and therefore to combat this in some cases when presenting paid search performance, they are mixing in brand search data with generics without splitting them out to make ‘search’ look better.

      This is just plain wrong. No matter how much the CEO or CMO likes the look of positive data especially through internal campaign tracked activity, as a media specialist they should be advising key stakeholders of the difference between both, letting them know that there’s no need to obsess around brand search performance because knowing what drives brand search is outside the SEM specialist remit and is a wider and bigger question / concern.

      A CEO or CMO asking an SEM specialist to increase brand search volume and constantly saying that “paid search drives the most conversions” than any other channel and that paid search should be given more budget (when brand and generics isn’t split out) is bad for business.

      I know that a lot of consultants and CMO’s are under pressure but there needs to be more effort from the SEM specialist and senior management team to understand what is driving search performance, splitting out generic and brand keywords clearly and focusing on driving incremental generic conversions leaving brand search volumes for another day.

      I’ve heard a lot of moaning and read a lot of articles (example here) about display specialists adding remarketing data into prospecting results and the fact that it needs to crack down, but not splitting out brand and generic search results is far worse and equally shambolic.

      There is an argument to have brand search data held in a completely different system to be used purely for online and offline brand attribution / to view halo effect, but what is clear is that brand and generic search data should never be mixed up on the same line and should always be kept separate.

      SEM specialists and consultants should be obsessing about how to improve generic paid search performance whether it’s ad copy performance or building long term strategies on building up their QS to achieve lower CPCs in the future and higher rankings which will in turn increase volume incrementally.

      There’s a time and a place to discuss brand search performance and it shouldn’t be when comparing overall digital channel by channel performance.

      spider.io-—-powering-accurate-analytics

      RTB brings a wealth of benefits to display advertising and one of the biggest features which is coming soon is the ability to auto optimise across different placements within a page based on historical in view rates. Yes, there is above the fold targeting but the volume isn’t there and there is still value from advertising below the fold.

      Many advertisers are throwing over 60% of their RTB spend and adserving down the drain because over half of the ads delivered simply did not get viewed. For those who accept PV, the wastage would be significantly higher as the channel gets attributed PV conversions where the ads didn’t even get viewed.

      This is where viewability technology comes in. Let’s look at the viewability technology marketplace and the test results off the back of a study to find the best partner:

      Firstly you need to identify what you class as ‘in view’ – some tools believe it or not have a default setting which cannot be changed of 1 pixel has to be in view to class the banner as ‘in view’. It’s recommended to set the values at 100% of ad surface viewed for a minimum of 4 seconds or 50% of ad surface for a minimum of 8 seconds.

      Project Sunblock

      The test didn’t even get off the ground as they only supported a solution where the creative would re-direct through their servers. This brings high risk of their technology affecting the ad delivery so this provider was ruled out

      Comscore

      This test also didn’t get off the ground as they struggled to send the necessary tagging over and their technology does not work with Chrome or IE browsers so for that reason alone this provider was ruled out

      Alenty

      • They have a pretty good offering and do offer very good customer support. Their tech didn’t affect impression volume and is compatible with all browsers
      • Test results show: Germany (35.8% in view), Portugal (40.7% in view), Poland (42.9% in view) and UK (29.2% in view)
      • They can use a DSP macro to pass back campaign name, strategy name, exchange name and domain name
      • They have a good reporting UI and can setup bespoke reports
      • You can customise your in view calculation in the UI and the data adjusts in real-time.
      • Compatible with DSPs
      • Infectious Media have been using the tech for a while now with optimisation success
      • Referring to their demo on http://www.alenty.com/en/demo/display-ads (which has since been removed), if you move the banner half off screen or have another window in front of the banner, the ad surface remains as 100% and visibility duration continues to go up which is a huge downfall of the technology because if you’re going to track viewability then you need to do it right.

      If it wasn’t for Spider.IO, this would have been a good alternative.

      Spider IO

      • Very good technology which is compatible with all browsers and didn’t affect impression volume
      • In view rate of 17%
      • They can use a DSP macro to pass back campaign name, strategy name, exchange name and domain name
      • They don’t have a reporting UI yet, but can supply bespoke daily reports with customised in view calcs
      • Technology is patent
      • Compatible with DSPs
      • As per their demo, their technology is truly ground breaking http://spider.io/vStp83jg6/
      • They offer log level data which can go into a DMP

      Spider.IO was the clear winner due to their patent tech, the demo which works and log level reporting. Alenty although the tech is not 100% is still a fantastic alternative.

      wasting-money-300x183

      Criteo have certainly become popular over the past few years with marketers. This is mainly due to them being sold into a no hassle site remarketing solution paying on a low risk CPC basis.

      What a lot of marketers and CEOs don’t know is that by using a managed site retargeting service like Criteo is not only lazy and inefficient, but it it’s also opening the business to data leakage which could have a damaging effect.

      Let’s break those three key points down further:

      1. Inefficient way of spending money – Criteo typically charge c 50p CPC yet using a DSP eg. MediaMath and Adacado together, the eCPC rate would be c 5p. Everything works back to an eCPM for display, so on a CTR of 1.5% you’re paying £5 CPM for the managed service via Criteo and only c 70p CPM for the media and dynamic creative service both on a self-service basis. Not only that, 1.5% is quite conservative for a dynamic remarketing CTR so as it goes up, so does Criteo’s eCPM.

      2. Lazy – it’s a lot easier to send a monthly invoice to accounts to pay but it does take a bit more intelligence, will power and proactiveness to get a self-service DSP on board and dynamic creative supplier. Display CRM would only be part of someone’s job and they’d typically apply a tight frequency cap and low CPM at the top of the funnel, but as you go lower in the funnel the frequency cap should be increased alongside the CPM as the users become more valuable to your business.

      3. Data leakage – allowing a third party to ‘manage’ your data for you is always risky that it could fall in the wrong hands eg. a competitor or data exchange.

      Something I haven’t mentioned is scale and volume, although Criteo do have a few exclusive publishers, this doesn’t match to any degree the sheer scale which a DSP such as MediaMath or DoubleClick Bid Manager bring to the table.

      images

      I went to the online video event which was put on by Adap.tv yesterday. It was certainly insightful and the panel were very open and clear about what they were looking to achieve and where the channel is going.

      The only disappointing element I found was that it was heavily focused towards publishers moaning about how much revenue they want to make from online video and discussing about a common currency. I think the online video space especially publishers really need to step back a bit and think about the role which online video has within the wider selection of media channels, advertiser demand and actual impact of online video and where this sits in relation to impact from TV.

      Marco from Vivaki touched on the fact that online video suppliers and publishers have given up trying to fight for the TV spend and it’s not a case of one or the other and more of a case of complimenting TV. Let’s look at this possibility in more detail:

      1. This would indicate that advertisers have tried online video in high volumes by turning off TV whilst the online video ads have been running and the impact hasn’t been the same – if it was or better, advertisers would not have thought twice about investing in online video at high volumes vs. TV over the past few years. Just to confirm that we only have to look at Youtube volumes to understand that there is inventory aplenty across the www.

      2. Complimenting TV would mean taking a fraction of what was the original vision of taking a significant chunk of TV spend

      3. If by delivering small volumes of online video, this would be hard to measure because 1. We all know that online video will never work on a post click basis and 2. We know that in order to view any movement on brand search / bottom line acquisitions, then you need to deliver high volume because otherwise an economics system would find it hard to pick up the online video activity especially as there are so many other factors which create noise.

      What I’d like to see are case studies or a concerted effort to work with clients to find out how impact differs vs. TV and also in terms of complimenting broadcast TV. Advertisers are not short of TV supply and when thinking of the main KPIs of a business which is to deliver a positive ROI, if they have extra budget why move it to online video when you can simply drive more TV ratings which would deliver a healthier ROI than online video.

      Tim from BskyB mentioned that he’d like CPMs of around £60 to £70 for online video which is ludicrous without having a thought for what the advertiser would get out of it impact wise. I’m expecting publishers who are greedy to find that advertisers can find users in the right environment at the right time across thousands of other premium publishers at a fraction of the cost thanks to Adap.tv. I think it’s fabulous that Adap.tv have come up with the market leader at this early stage and it’ll be interesting to see how the impact value weighs up with broadcast TV or with any other digital channel.

      If online video impact is considerably less vs broadcast TV, could this simply be down to consumer experience and someone who watches video online wants to watch exactly what they want and will not accept anything to get in their way, yet when you look at broadcast TV, you tend to have more than one person watching TV with no ability to forward ads on a 40” or so Samsung HD TV. Either way, it’s an absolutely crucial step which needs to take place soon and large spend across online video may not be unlocked until live TV is adserved and streamed through your 40” HD TVs.

      Online video is double the price than TV when looking at the in target CPM (which is crazy alone and indicates that the online video CPMs really do need to come down) but the level of impact / value online video delivers to clients in the form of revenue is still unknown and until this key element has been unlocked, the online video spend potential will remain locked up.