Retargetting is so Flintstones



By Charlie Tarzian, Founder, The Big Willow

So we’re at least 7 years into exchange based media and we’re still complaining about retargeting. Consumer complaints are that it is mindless or creepy. And on the professional side, clients want more control over who to retarget and when.

Meanwhile, back at the zombie ranch of retargeting there has been very little innovation or progress made on some basic requirements that would change the game.

The biggest issue is lack of integration into the enterprise. And by enterprise I mean those data repositories and systems that run any company. The fact that brands continue to chase us with acquisition based messaging even after we have purchased is clearly a missed opportunity. Which begs the question: Is retargeting that misunderstood that it falls to the bottom of the data integration wish list? Do brands understand the magnificent fail of not knowing who their new customers are and the state of play between any one consumer and their company?

Without question brands need to start paying attention to this continual consumer aggravation.

Recently, a B2B client said in a meeting: We are only really interested in investing in our targeted client list. We want to know when they come to our site, we want to know when we serve them ads, when they open and click through our ads, when they follow and share our social links, etc…

So why, she asked, are we retargeting everyone that comes to our site? Our targeted list represents less than 10% of everyone that comes – so why can’t we suppress retargeting to the other 90% of the audience we are not interested in at this time? And what about if we want to retarget based on which part of the site and which product they were engaged with?

Her company’s agency responded: That’s not how it works. There is no way to discriminate. Anyone that comes to the site becomes part of the retargeting pool.

So – indiscriminant retargeting is what it shall be!

Now, on the other side, retargeting relies on building sizable pools of audience to drive the cost of bidding down – meaning the more you have in the pool and the more you have to choose from the better chance you have of winning a certain percentage of your bids and of keeping the costs down. Retargeting buys can be two times the cost on a CPM basis when compared to straight CPM buys. We get that. But retargeting parameters should be no different than how you would set up any DSP-based campaign. You should be able to create whitelists with rules we use in any campaign: only these IP addresses, or these devices or cookies, or customers that have contracts coming up, etc… I only want to target those and with the right context.

If you think about it – instead of relying on the primitive, non-evolved way retargeting is done today, we should be thinking about moving the heavy lifting of retargeting to the same data-driven approach we take through our DMP’s-to-DSP’s-to-ad servers process. That’s how we operate the foundational aspects of our media stack today, so why can’t we use the same stack to inject logic, filtering and knowledge into retargeting.

I can tell you we are working on this issue and I have to imagine others are as well. We call it Filtered Retargeting and to be honest – it is not retargeting as much as it is sequencing messaging based on using historical data. Historical can mean 10 minutes, 10 days or 10 weeks – but the strategy relies on being up to date with previous interactions across data sets and systems. But that is just one dimension.

The other is getting client organizations to architect how key customer data gets into the marketing stack with the aforementioned frequency. When someone makes their first purchase, reactivates, buys a new service, upgrades, etc… the marketing operations world must be updated and rules put in place to allow a change in how we communicate to that individual and/or company. This is the promise of both DMP’s and of an ALWAYS ON marketing stack.

All to say, retargeting has withered on the vine for so long and yet could be so much more effective in enhancing relationships.

Let’s put some of that great thinking that has created so many innovations and breakthroughs into this issue so we can stop talking about it. Selfish as it is, I am tired of retargeting being the subject of dinner party conversations!

What are your thoughts? And who do you think is and/or should be solving for this lack of progress?

Big Data Trends | Strategies driving investments in data

imgresLast year, IDG published a study 2015 Big Data and Analytics, Insights into Initiatives and Strategies Driving Data Investments that was based on interviews with 1,139 IT leaders from nine industries with high tech (16%), government (12%), financial services (11%) and manufacturing (9%) being the top four industries surveyed.

 

Key findings Infographic:

Below are a few key take-aways, the report is embedded at the bottom of this post:

  • 80% of enterprises surveyed have data-driven and big data projects in implementing or planning stages today versus 63% of SMBs. 37% of enterprises have deployed data-driven projects in the last year, and 18% are in the process of implementing or piloting projects as of today.
  • 83% of organizations prioritized structured data initiatives as critical or high priority in 2015, and 36% increased their budgets for data-driven initiatives.
  • Improving the quality of decision making (61%), improving planning and forecasting (57%) and increasing the speed of decision making (51%) are the three most common business goals and objectives driving data-driven initiatives in organizations today. The following graphic compares which business initiatives are driving big data investment and the positive impact of big data on each.
  • 36% of enterprises expect their IT budget allocations for data-driven initiatives increased in 2015, 41% anticipated budget levels would remain at current levels and 21% aren’t sure. Only 3% say data-driven and big data-related project funding will decrease.
  • Data analytics continues to accelerate as the most preferred solution for gaining greater business insight and value from data, with this category increasing in importance 55% from 2014 survey results. In enterprises, data analytics (65%), visual dashboards (47%), data mining (43%), data warehousing (40%) and data quality (39%) are the five most preferred solutions. In my discussions with CIOs in financial services and manufacturing companies, the shift away from pre-built dashboards with common metrics and key performance indicators (KPIs) to the flexibility of defining their own data models in metrics is the future. Dashboards in financial institutions need to have the flexibility of quickly integrating entire new metrics and KPIs as their business models change. For manufacturers, the need for interpreting shop floor data to financial results is what’s driving data analysis and dashboards in the many manufacturing industries adopting analytics today.
  • The number of enterprises who have deployed/implemented data-driven projects increased 125% in the last year, with 42% still planning data implementations as of today. The following graphic from the study illustrates a comparison of 2014 and 2015 plans for considering, planning and implementing data-driven projects.

 

View the report here:

Download the IDG Report: 2015 Big Data and Analytics, Insights into Initiatives and Strategies Driving Data Investments

Big Data Trends | Strategies driving investments in data

IDG Enterprise logoLast year, IDG published a study 2015 Big Data and Analytics, Insights into Initiatives and Strategies Driving Data Investments that was based on interviews with 1,139 IT leaders from nine industries with high tech (16%), government (12%), financial services (11%) and manufacturing (9%) being the top four industries surveyed.

 

Key findings Infographic:

Below are a few key take-aways, the report is embedded at the bottom of this post:

  • 80% of enterprises surveyed have data-driven and big data projects in implementing or planning stages today versus 63% of SMBs. 37% of enterprises have deployed data-driven projects in the last year, and 18% are in the process of implementing or piloting projects as of today.
  • 83% of organizations prioritized structured data initiatives as critical or high priority in 2015, and 36% increased their budgets for data-driven initiatives.
  • Improving the quality of decision making (61%), improving planning and forecasting (57%) and increasing the speed of decision making (51%) are the three most common business goals and objectives driving data-driven initiatives in organizations today. The following graphic compares which business initiatives are driving big data investment and the positive impact of big data on each.
  • 36% of enterprises expect their IT budget allocations for data-driven initiatives increased in 2015, 41% anticipated budget levels would remain at current levels and 21% aren’t sure. Only 3% say data-driven and big data-related project funding will decrease.
  • Data analytics continues to accelerate as the most preferred solution for gaining greater business insight and value from data, with this category increasing in importance 55% from 2014 survey results. In enterprises, data analytics (65%), visual dashboards (47%), data mining (43%), data warehousing (40%) and data quality (39%) are the five most preferred solutions. In my discussions with CIOs in financial services and manufacturing companies, the shift away from pre-built dashboards with common metrics and key performance indicators (KPIs) to the flexibility of defining their own data models in metrics is the future. Dashboards in financial institutions need to have the flexibility of quickly integrating entire new metrics and KPIs as their business models change. For manufacturers, the need for interpreting shop floor data to financial results is what’s driving data analysis and dashboards in the many manufacturing industries adopting analytics today.
  • The number of enterprises who have deployed/implemented data-driven projects increased 125% in the last year, with 42% still planning data implementations as of today. The following graphic from the study illustrates a comparison of 2014 and 2015 plans for considering, planning and implementing data-driven projects.

 

View the report here:

Download the IDG Report: 2015 Big Data and Analytics, Insights into Initiatives and Strategies Driving Data Investments

3 Steps to Make Your Business Data Work for Everyone | by Marius Moscovici

b2c_fbOver the past decade, advances in cloud technologies and big data created room for more data scientists in business. At first, specialists seemed like the proper solution — give the best data scientists access, and let them tell the rest of us how to use it.

Unfortunately, funneling data through a few individuals creates over dependence on already overworked specialists and prevents the rest of the company from using the data as effectively as possible. The data scientists might be able to understand the data better than anyone else, but there will never be enough data scientists to go around.

Spread the Data Around

Asking data scientists to act as intermediaries between your other employees and needed information bottlenecks their creative exploration, and it makes average business users wait longer than necessary for answers. As data analytics spending increases, companies need to make the most of the information they already have.

Think of data as a buffet: If you let only three people eat from the buffet and give everyone else ham sandwiches, you’re not making the most of your resources. You want everyone to eat from the buffet — the food is more useful, a bigger variety is consumed, and everyone gets exactly what he needs.

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In a perfect solution, users can access the data they need to be more efficient, while data scientists enjoy the freedom to address more complex functions. Following these three simple steps will help everyone in your business use data to its full potential.

1. Add a Push Intelligence Layer

Push intelligence uncovers anomalies in data and alerts users when necessary. If something in your company’s data is changing, you want the right employee to know what it is and what action to take without having to sift through complex piles of data to find out.

2. Educate Employees

The more information employees have to keep in mind when working, the more complex their jobs become. Your employees need to understand the importance of using data effectively and being part of a data-driven business.

3. Use the Right Tools

Look for vendors and tools that can help your business digest data and present it to the company in a shareable, understandable format. The best business intelligence software marries modern BI functions with social aspects to engage employees.

The more efficient your use of data, the better collaboration you will see among team members as everyone gets access to information in real time and makes educated decisions as a cohesive unit.

Your software needs to support everyone in your company, not just a small percentage of specialists.

Sourceb2c_fb

 Written by: Marius Moscovici

Marius Moscovici is the founder and CEO of Metric Insights. He founded the company in 2010 to transform the way business intelligence is performed so organizations of any size can quickly and easily deploy powerful analytics. Marius has over 20 years of experience in analytics and data warehousing and was previously the co-founder and CEO of Integral Results, a leading business intelligence consulting company that was acquired by Idea Integration.

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2016 Marketing Requires High Quality Data

Do Your 2016 Marketing Resolutions Include Achieving High Quality Data?

58% of marketers cite personalizing the customer experience as the most important objective of a data-driven marketing strategy, yet 57% say that improving data quality is the most challenging obstacle to data-driven marketing success. Data quality issues have challenged organizations for years, but it has now become a huge issue and responsibility that is impacting every area of marketing.

Source: 2016 Marketing Requires High Quality Data

Exploding Three Common Myths about Real-Time Interaction Management | Pega

Companies know that “spray and pray” marketing doesn’t work anymore and are turning to real-time interaction management (RTIM) technology in hopes it will increase offer uptake.

Source: Exploding Three Common Myths about Real-Time Interaction Management | Pega

3 Levels of Data Analysis to Revitalize Your Automated Email Programs 

Optimizing for incremental percentage gains on your email statistics could make a monumental difference in revenue and brand perception in the following weeks and months.

Source: 3 Levels of Data Analysis to Revitalize Your Automated Email Programs 

Digital marketing in 2015: Data quality will emerge as a key issue for marketers

By Andrew Frank

The dawn of a new year is a traditional time for predictions. Gartner published many back in November, including these from the Marketing Leaders team (subscription required), but not all of our predictions made it into our reports. For one thing, the ones we published looked at longer horizon than just the coming year. They were also fairly specific, developed, and vetted by extensive peer review.

Here are some more general and unfiltered personal thoughts for what I believe we can expect from digital marketing in 2015:
Rising consumer spending and digital marketing budgets will fuel investment in advanced customer experiences. This one is supported by quite a bit of data we gathered in 2014 (see Gartner for Marketing Leaders Research Survey Results, 2014 (subscription required)), particularly the finding that eighty-nine percent of companies surveyed plan to compete primarily on the basis of the customer experience by 2016. Combined with recent U.S. economic indicators signaling growth in 2015, this means most enterprises can count on the availability of funds and mandates to innovate around customer experiences, especially digital ones.

Data quality will emerge as a key issue for marketers. In 2014 concern about issues of quality in digital advertising grew into a tidal wave, culminating in the recent revelation by Google that over 56 percent of its ads on its various display advertising platforms are never viewed. But the issue of data quality has yet to receive proportional attention, despite its equal potential to compromise marketing effectiveness. Both first-party and third-party data are susceptible. Many organizations take for granted that data about prospects and customers in their own marketing automation and CRM systems is incorrect, incomplete, duplicated, or our-of-date. This is one of the reasons they turn to third parties. But third-party data accumulated by aggregators and sold to marketers that can suffer from similar defects, as well as problems with opaque and dubious privacy protection policies applied to data collection and the potential that, as marketing technologies tackle the problems of click and impression fraud, fraudsters will find fertile ground in data markets. As marketers are compelled to rely more on data, the need to implement quality checks will grow dramatically.

Consumers will become more conscious and protective of mobile and social data. High profile exploits like the iCloud celebrity photo scandal awakened consumers to the dangers of unchecked data collection enabled by social and mobile apps in 2014. Apple responded with some significant new privacy features in iOS 8. One far-reaching but underreported example involved its handling of apps that record location data while not currently active. These are now revealed through warning messages that alert users and allow them to revoke these apps’ permission to do so. This is bound to reduce the unconscious reporting of location data from the current norm. Facebook also signaled its sensitivity to such issues by highlighting new privacy policies of its own, including setting the default audience for first-time posters to “Friends” rather than “Public.” Although controlling privacy still remains an obscure challenge for most mobile and social users, expect more savvy, evasive, and occasionally outraged digital consumers in 2015.

Digital video will continue to converge with TV, driving the sharpest increase yet in share of ad spending. Video still rules the roost for brand-building with all that sight, sound, and motion, but Nielsen rattled the TV world in when it reported that 2014 saw a significantly more precipitous decline in TV viewing than any previous year. Media companies such as CBS and HBO responded with online versions of programming, which is sure to accelerate the growth in non-traditional viewing in 2015. This all adds up to a major shift in the disposition of TV advertising budgets – which are still almost twice the size of digital – to include digital video alternatives. This will in turn drive new formats as advertisers look to take advantage of targeting and interactivity. Keep an eye on dynamic in-video product placement, an emerging form that’s poised to heat up as a key native advertising format in 2015. (More on this soon.)

Marketing scientists will make significant breakthroughs in the algorithmic detection of nuanced attitudes and opinions entirely from online behaviors. For all the advances in big data and digital marketing, marketers are still somewhat in the dark in terms of their ability to really understand what makes customers respond. In 2015 we can expect more math and science graduates to embark on marketing careers, lured by the promise of attacking such fascinating problems and reaping the rewards of fundamental breakthroughs in effective personalized communication that seem tantalizingly close.

— Andrew Frank, research VP & Distinguished analyst, Gartner. He specializes in best practices for data-driven marketing and serves digital marketing leaders.

For more blogs by Andrew Frank, log on to http://blogs.gartner.com/