Filed under: A/B Testing, Actionable Insights, CRM, Cloud Computing, Data, Datarati, Email Marketing, Lead Generation, Lead Nurturing, Lead Scoring, Loyalty, Marketing Automation, Optimisation, Predicitive Modelling, Retention, SMS, Segmentation, Software as a Service, Statistics, Surveys, Technology, Testing, Web Analytics | Tags: Breakfast Briefing, Database Marketing, Datarati, Marketing Automation Database, Marketo
Join Australia’s leading digital marketers for the DATA-DRIVEN MARKETING EVENT OF THE YEAR!
At this FREE Breakfast Briefing, hosted by Brad Howarth, the ex-Technology & Marketing Editor of Business Review Weekly (BRW) Magazine, you will be briefed by the industry’s leading data-driven marketers on how to use digital behavioural data in both acquisition and retention campaigns to generate more revenue faster for your organisation!
To REGISTER for this event, please contact:
Will Scully-Power
E: will.sp@datarati.com.au
M: 0400 828 866
Filed under: A/B Testing, Actionable Insights, Algorithms, Analytics, Behavioural Targeting, Business Intelligence, CRM, Call Centre Data, Cloud Computing, Data, Datarati, Email Marketing, Lead Generation, Lead Nurturing, Lead Scoring, Loyalty, Marketing Automation, Multivariate Testing, Optimisation, Predicitive Modelling, Segmentation, Software as a Service, Statistics, Surveys, Technology, Testing, Web Analytics | Tags: Behavioural Data, Datarati, Elevator Pitch
I got asked this morning at an Ad-tech event in Sydney what our company ‘Datarati’ does in 30 seconds or less so here goes the elevator pitch:
Datarati helps smart marketers unlock the value of digital behavioral data, by allowing them to execute more personalised and effective email marketing and web personalisation through the use of a marketing automation database (MAD).
Today’s smart data-driven marketers use a MAD to leverage the wealth of demographic/profile data and implicit/behavioural data that online visitors and customers generate as they react to a company’s digital marketing campaigns and interact with its landing pages, microsites and websites.
By centralising all this data into a MAD, marketers can now access and have full visibility into all multi-channel campaign data including:
- Email Marketing data
- Paid Search data
- Paid Display data
- Landing Page, Microsite, Website data
- Social Media data (twitter, facebook, linkedin)
- Telemarketing data (customer service + support)
Why is this so exciting for marketers?
Well today, marketers are living in Excel Hell! They try to collect and collate multiple data sets in Microsoft Excel which are delivered to them by their multiple rostered agencies and or vendors.
With all of these data silios created how can marketers know which channels are most effective in driving leads, opportunities and ultimate conversions/revenue for their organisations?
With a MAD all of this data is centralised into one database where the marketer can execute digital campaigns from end-to-end including:
- Data Segmentation aka List building (Demographic + Behavioural) data
- Email, Form + Landing page creation & execution
- Data Scoring
- Data Nurturing (Drip based Acquisition, Retention, Loyalty Campaigns Campaigns)
- Data Testing / Optimisation
- Reporting & Analytics
- Integration into their CRM database for closed loop ROI reporting & analysis e.g. Salesforce.com, Netsuite, Microsoft, Oracle, Siebel, SAP, etc.
Oh and P.S you need no IT involvement or support as the MAD is delivered as Software as a Service (SaaS) over a web browser 
and payable by the number of records you have in your database.
I speak quickly, so there goes my 30 seconds
Also, we are holding the “DATA”‘ event of the year for all Digital Marketers and their Digital Agencies in Sydney in a few weeks time.
Email me if you’re interested in attending!
Filed under: Predicitive Modelling | Tags: Attribution Modeling, Campaign Attribution, Last Click Attribution
If you asked a marketing manager what the result of their campaigns is, everyone would want to take credit for the wins. The issue is attribution modeling and being able to assign credit in multi-channel programs. How do you know you have the right metrics in place to put the right resources into the funnel?
They don’t expect to give us an answer today, but there’s a lot of solid work being done in attribution modeling. How do you coordinate systems and data for attribution modeling. How do you do testing to see the impact of what you’re doing on the conversion funnel? And one overlooked issue they’ll try to approach is governance.
Is paid search overrated and what are the panelists’ organizations doing around attribution modeling. Gary starts by saying if you go back to John Wannamaker’s quote, the traditional ad industry has been rife with wastage. The strategy is to target ad dollars on perceived targets but they’re not always the right ones.
About 15 percent of media investment is in digital. Forrester also expects the number to go to 25 percent in the next few years, but Gary thinks that’s an underestimation. Last click measurement may lead to poor investment decisions and slow the digital investment. Paid search is getting too much or too little credit.
More: http://www.bruceclay.com/blog/archives/2009/08/last-click-is-dead.html
Filed under: Predicitive Modelling | Tags: Behavioural Data, Netmining, Propensity Modelling
Advertising and marketing’s dependence on technology has prompted Innovation Interactive to spend the past six months integrating a suite of applications and behavioral targeting platform developed by Netmining into offerings from 360i and SearchIgnite.
Netmining measures and analyses browsing behavior, recent and frequent visits, past purchase history, search queries, demographics and a calculated score that the company refers to as “propensity to buy.
“This data is used to craft individual visitor profiles without personally identifiable information (PII), so marketers can reach consumers beyond target segments and tailor ads to each user’s unique interests and point in the purchase funnel.
More: http://www.mediapost.com/publications/?fa=Articles.showArticle&art_aid=111090
Filed under: Analytics, Business Intelligence, Data, Datarati, Predicitive Modelling, Segmentation | Tags: IBM, SPSS
IBM is buying analytics software and solutions provider SPSS in an all cash transaction at a price of $50/share – a 42 percent premium to Monday’s closing price of $35.09 on Nasdaq – resulting in a total cash consideration in the merger of approximately $1.2 billion.
The acquisition is subject to SPSS shareholder approval, regulatory clearances and other closing conditions, and is expected to close later in the second half of 2009.
More: http://www.washingtonpost.com/wp-dyn/content/article/2009/07/28/AR2009072800858.html
Filed under: Analytics, Data, Predicitive Modelling, Segmentation | Tags: Billboard advertising, CommunicAsia 2009
Singapore’s Agency for Science, Technology, and Research (A*Star) has developed a gender recognition system that could change the way advertising works in the future.
The technology uses sophisticated algorithms to differentiate facial features of males and females. However, unlike Face Detection 3.0, which is employed in point-and-shoots such as the Fujifilm FinePix F200EXR, the gender recognition system can only detect faces that are facing the camera.
The system can also track statistics such as the duration the viewer spends in front of the display.
Filed under: Business Intelligence, Predicitive Modelling, Social Media, Websites | Tags: Social Media Data, Travelocity, Victoria Treyger
Victoria Treyger, the CMO of Travelocity.com wayed in being a digital data driven marketer.
In Treyger’s case, the amount of data can seem overwhelming to an outsider.
In addition to Google—which provides information about competitors and industry trends, she culls consumer data from Hitwise, Compete and Comscore among others and then looks at how consumers behave on Travelocity’s site—namely shopping and booking patterns.
For now, social media is primarily a marketing tool rather than a way to wring more data out of consumers, though Treyger said when the company relaunched its Web site in May, it used Twitter to get a read on how consumers were taking to the new site. (They liked it.)
In addition, for the last three years Treyger has made use of mixed-media modeling, a sort of dashboard that uses statistics and econometrics to determine which media mix delivers the best ROI.
For Treyger, a veteran marketer from Amazon, being immersed in such data is second nature. “Relying upon all of these different data sources is a regular part of the job.
It’s kind of like having coffee in the morning,” she said. “I wouldn’t think about making any key business decisions without relying on data, but it’s not most of my job, it’s part of my job.”
“Most of the marketers interviewed in her recent survey relied upon digital agencies—often as many as six or seven—to collect and interpret such data”.
“They’re not being managed in the way creative agencies are managed or the way integrated or above-the-line agencies are being managed,” Seid said of the digital agencies”.
“There’s nobody inside that has the expertise that’s on par or superior to the digital agencies. So the digital agencies are more or less setting the agenda.”
More: http://www.brandweek.com/bw/superbrands/article_empathetic.html
Filed under: Behavioural Targeting, Optimisation, Predicitive Modelling, Web Analytics | Tags: Omniture, Quantivo, SiteCatalyst
On-demand behavioral analytics provider Quantivo recently announced a product to integrate with Omniture’s Web analytics solution SiteCatalyst.
Whereas Omniture tracks the number of visitors to a company’s Web site, Quantivo’s integration product, which Chief Executive Officer Brian Kelly calls “the adapter,” will help SiteCatalyst users make sense of the identities of those visitors.
In doing so, companies can better understand customer behavioral patterns — based on Web-site visits, transactions, marketing responses, and other events — and improve the targeting accuracy of marketing messages and on-site recommendations.
According to Kelly, trends in online customer behaviors are emerging, changing, and disappearing so quickly that companies now demand a solution that can handle a sudden influx of data and quickly turn that data into something meaningful.
“Big massive trends develop and diminish within a week,” Kelly says. “You don’t have time for some Ph.D. statistician to build a predictive model. [Oftentimes], it’s not something you can predict.”
In other words, while historical transaction data (and common sense) may help predict that sweater sales rise in the winter, that information is of little use for a retailer hoping to foresee which color will be most popular.
More: http://www.linkedin.com/news?actionBar=&sik=1243508631845&aIdx=0&articleID=38491000
Filed under: Behavioural Targeting, Numerati, Predicitive Modelling, Search | Tags: Algorithm, Google, Predictive Modeling
Concerned a brain drain could hurt its long-term ability to compete, Google Inc. is tackling the problem with its typical tool: an algorithm.
The Internet search giant recently began crunching data from employee reviews and promotion and pay histories in a mathematical formula Google says can identify which of its 20,000 employees are most likely to quit.
Google officials are reluctant to share details of the formula, which is still being tested. The inputs include information from surveys and peer reviews, and Google says the algorithm already has identified employees who felt underused, a key complaint among those who contemplate leaving.
More: http://online.wsj.com/article/SB124269038041932531.html#articleTabs%3Darticle
