The most effective way for companies to optimize their marketing campaigns today is with predictive analytics. Because companies are consistently dealing with smaller budgets when it comes to marketing, predicting consumers’ responses to your efforts is essential to your success.
Trying to manually analyze each marketing campaign is a task not even the most effective marketing department can handle.
As promised a couple of weeks ago, I am very happy to share Part 2 of a webcast series highlighting how SAS participates in the space of digital analytics for data-driven marketing with applications for personalization and attribution. The majority of technologies in use today for digital personalization have generally failed to effectively use predictive analytics to offer customers a contextualized digital experience. Moving on to our second subject of interest, we transition to the wonderful world of marketing attribution. SAS Customer Intelligence offers a one-stop modern marketing platform to comprehensively support the objectives of predictive personalization and algorithmic attribution - from digital data collection, management, predictive analytics, omnichannel journey orchestration, delivery across online and offline channels, and measurement. On April 19 at SAS Global Forum 2016, SAS Customer Intelligence 360 will make its debut, and subjects like digital intelligence and predictive personalization will be primary topics. Welcome to Customer Intelligence, a blog for anyone who is looking for ways to improve the business of marketing and communicating with customers. The blog content appearing on this site does not necessarily represent the opinions of SAS. The report analyzes the key trends and dynamics that are shaping the predictive analytics market as it evolves from a nascent market to mainstream, must-have technology. B2B predictive analytics is an emerging market with less than a $100M in aggregate vendor revenue. We forecast rapid growth, with 36.8% of high growth companies investing in predictive analytics over the next 12 months.
As the market accelerates, buyers need a framework to reduce adoption risk and demonstrate ROI. Compare vendors against your starting use case requirements and talk to customers that are in production in that specific area. Marketing teams must be prepared for rapid iteration during the pilot and production phases. We anticipate vendor consolidation so buyers must consider the long term viability of vendors.
About the author: Craig Rosenberg is a co-founder and Chief Analyst at TOPO and blogger on the Funnelholic sales and marketing blog. Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. This landscape is a first pass at understanding the range of vertical-specific predictive analytics solutions available on the market, with emphasis on startups and newer incumbents (rather than legacy providers such as SAS, FICO, and IBM’s SPSS and Watson products).
Clipping is a handy way to collect and organize the most important slides from a presentation. Our analytical solutions and services help the banks in identifying high value prospects & existing customer segments and predicting future credit behavior to improve profitability and optimize capital disbursement. Marketing Analytics: Identify right customer segments, offer right products and increase customer life time value. Design and Development – Comprehensive services for marketing and risk management models across customer life cycle using advanced statistical techniques. Audit, Validation & Recalibration – Independent audit services for your existing models to validate them for the stated business purpose and if they are supported by the data. Analytics Centre of Excellence – Dedicated teams comprising of analytical professionals with expertise in building banking models to work exclusively on your ongoing requirements.
In the first segment of this two-part series on predictive analytics, I discussed the potential of analytics for providing a better ROI for marketers as well as how to select the right databases to analyze and what to do with them once they were identified.
Andrew Gill, CEO of Kred, was first impressed with the potential of predictive analytics when he saw a presentation about how law enforcement organizations were using Big Data to predict potential criminal activity and then used those predictions to make arrests leading to convictions. One example of easily tracked and segmented client’s characteristics is geographic location.


The location-based example that Bulat provides is a good example of why context as well as content matters.
The second reason that predictive analytics is gaining traction is that new technologies are being developed every day to take advantage of Big Data.
Kardon concludes, “The next generation of marketing leaders will be those who effectively harness the power inherent in big data, and the early adapters are already embracing predictive analytics.
This method of marketing allows companies to predict consumer behavior, allowing the company to determine its potential success and how much money should be spent on that particular campaign.
Finding the right way to predict the response will give executives the proof they need to justify the amount of money each campaign costs. It takes effective planning and skills to determine the exact marketing methods that are going to work, as well as what category of consumers your efforts will work on. When you use methods to predict the preferences and habits of consumers, you will be able to better predict the marketing efforts that will work. Having the ability to track customer behavior, patterns and various other data from past efforts will allow you to use that data to effectively plan for the future. Honestly, it is increasingly important to predict how customers will behave so you can personalize experiences with relevance. Most are based on simple rules-based recommendations, segmentation and targeting that are usually limited to a single customer touch point. At the very core of this topic, modern marketers recognize that customers expect brands to deliver relevant conversations across all channels at any given moment. SAS strongly believes three years later that we are living in a game-changing moment within digital analytics. This new offering will drive unprecedented innovation in customer analytics and data-driven marketing, putting predictive analytical intelligence directly in the hands of digital and integrated marketers responsible for the customer experience.
And we hope to inspire the use of analytics for everything from multi-level marketing to social media campaigns.
Fueled by drivers such as big data, SaaS delivery models, and data-driven marketing and sales, predictive analytics garners a tremendous amount of attention, particularly given how few customers are in actual production. The report provides buyers with specific, actionable guidance for understanding, evaluating, and adopting predictive technology. The predictive analytics market for B2B is still in the early market stage of the technology adoption life cycle. While it’s still a nascent market, the promise of using predictive analytics to increase revenue growth is real. A number of dynamics make understanding the overall landscape and specific vendor capabilities a challenge. For most organizations, the best predictive analytics starting point is lead scoring and building ideal customer profiles (ICPs) for target account selection. Marketing organizations should develop requirements against their starting point use case and evaluate vendors against those requirements.
Despite SaaS delivery models, predictive analytics does require implementation and adoption resources.
Over the next 3-5 years we expect to see market consolidation as products and datasets commoditize, larger marketing technology vendors get acquisitive and many predictive analytics vendors either pivot or go out of business.
The goal in compiling this landscape is to understand which verticals are supersaturated with new predictive solutions (e.g.
With deep insights into local market conditions, we offer analytical interpretations even when there is paucity of data.
As these systems mature, predictive analytics will undoubtedly play an ever-larger marketing role. With predictive analytics, companies are able to target the right customers at the right time. Wasting time and money on marketing plans that are not going to be effective only serves to give the competition the upper hand.


Predictive analytics mean making an informed decision about your marketing efforts based on past purchases and habits. Using analytics the right way will allow you to increase your sales, realize a greater return on investment and increase your customer base.
The deeper your understanding of customer behavior and lifestyle preferences, the more impactful personalization can be. Despite some use of predictive techniques, digital experience delivery platforms are behind in incorporating predictive analytics to contextualize experiences using 1st-, 2nd- and 3rd-party customer data. In my opinion, I believe the usage of digital data mining and predictive analytics to prioritize and inform the marketing teams on what to test, and to analytically define segment audiences prior to assigning test cells, is a massive opportunity.
The challenge is to uncover the interactions that drive conversions through integrated measurement and insights. Marketers are being enabled with approachable and self-service analytic capabilities, and this trend directly impacts our ability to improve our approaches to problems like attribution analysis. While the hype can sometimes be excessive, early adopters are realizing demonstrable ROI as they use statistical modeling, machine learning, and scoring technologies to identify and prioritize accounts, leads, contacts, and customers at specific points in the marketing and sales funnel.
Early adopters have seen compelling, demonstrable ROI from their predictive analytics programs. We recommend demonstrating success in one of these two areas before moving to other areas in the revenue chain. Buyers should also work with vendors to speak with customers that are in production on a use case that closely mirrors yours. The most successful early adopters rapidly iterate on data inputs and scoring models during the pilot.
As I noted in a previous post, Staples, the office supply company, became the poster child for this kind of personalization strategy when Jennifer Valentino-DeVries, Jeremy Singer-Vine, and Ashkan Soltani revealed that “the Staples Inc. Taking a close look at the patterns of the past data allows you to make decisions that are geared toward the future. However, digital personalization at the individual level remains elusive for most enterprises who face challenges in data management, analytics, measurement, and execution. As customer interactions spread across fragmented touch points and consumers demand seamless and relevant experiences, content-oriented marketers have been forced to re-evaluate their strategies for engagement. Marketers are very creative, and can imagine hundreds of different testing ideas – which tests do we prioritize if we cannot run them all? However, rules-based methods of attribution channel weighting continue to be far more popular in the industry to date, which contradicts the recent analytic approachability trend.
These early adopter wins are spurring investment from a larger swath of the market, particularly among companies with demonstrated marketing automation success and high volume funnels. As part of this research effort, the TOPO Analyst team performed detailed analysis on vendors such as 6sense, EverString, Fliptop, Infer, Lattice Engines, Leadspace, and Mintigo. Taking the time to understand customers’ behavior in the past will pave the way for your company’s future.
But the complexity, pace and volume of modern marketing easily overwhelms traditional planning and design approaches that rely on historical conventions, myopic single-channel perspectives and sequential act-and-learn iteration. This is where advanced analytics can help inform our strategies in support of content optimization, as it allows the data to prioritize our strategy, and help us focus on what is important. Third, very few vendors are able to articulate how they are different from the competition. Most importantly, they should be prepared to work iteratively with vendors on building and maintaining models. More than that, Staples appeared to consider the person’s distance from a rival brick-and-mortar store, either OfficeMax Inc.



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