analytics

4月 212016
 

When something comes between you and your customers – like not having product in-stock or providing offers that aren't relevant to the customer – it causes delays and makes it harder to complete transactions in a satisfying way. But, an inexpensive sensor, beacon or Radio Frequency Identification (RFID) tag placed […]

Don’t let anything come between you and your customers – except IoT sensors was published on SAS Voices.

4月 202016
 

Just last weekend, I was considering buying a new camera lens. I already had a few brands in mind, so I looked online at their websites to learn more about their product information. I was able to conduct a comparison on different brands and lenses to narrow down to a specific 50mm lens provided by a major brand. I added the lens to my cart online, but wanted to get a closer look of it, so I chatted online with a representative to see if there were any lenses available at stores near me. This digital channel was my first point of interaction with the brand, but what impact did that have on my buying experience? Would responsive design come479424735 into play? Would the brand proactively contact me about similar products? Or would they simply react to inquiries that I had as a consumer? But today’s consumers expect immediate, individualized messages – would this brand deliver?

The fact of the matter is that a lot of brands don’t have the capabilities to modify messages, offers and interactions across channels, devices and points in time so that they are more relevant to the end consumer.

 Enter SAS

SAS Customer Intelligence 360, launching this month to the marketplace, offers an all-encompassing view of customers no matter how they choose to engage with you across digital properties.

A complete customer view

SAS Customer Intelligence 360 can give you detailed insights from digital channels customers interact with to create the most effective and relevant actions. The solution rapidly transforms digital data into a complete 360-degree view of the customer, meeting each customer’s needs at the right time, place and in proper context. Multiple decision-making methods, such as predictive models and multivariate tests, help ensure that customers gets the most relevant and personalized offers.

Data integration

Data is also easy to integrate with many offline customer channels though SAS Customer Intelligence 360 and its customer decision hub. Customer interactions are based on previous engagements on all other platforms. The data hub is able to convert all of this into customer-focused actions. With this data integration, the Customer_decision_hubbrand is able to gather my interactions and information from all available sources; not just the website, but the call center, mobile apps, social media and point of sale.

Offline customer data can be appended to digital data to further augment the view of me as a customer. These data sources, typically demographic or transactional in nature, gives marketers valuable insight into a customer’s true needs in order to create more relevant offers, better targeted activities and more efficient use of marketing resources. This capability allows the brand to see me more than just page clicks. They’ll see me as a father with young children, interested in photography and seeking to buy a 50mm lens to capture fleeting family moments.

Insights into future actions

You don’t need to be a data scientist to harness the power of predictive marketing; SAS Customer Intelligence 360 includes guided analytics to provide marketers a forward-looking view of customer journeys. This enables them to better understand business drivers and incorporate them into segmentation, optimization and other analytic techniques. Marketers can better forecast how customers will perform in the future. The solution acts as the data scientist – enabling marketers to become more efficient and effective in the analytical techniques they embed into marketing initiatives.

Web data collection

Each web page is embedded with a single line of HTML that automatically collects page information without expensive tagging. With this feature, the webpage configuration might change simultaneously with what I click on, the order and timing of my clicks, each keystroke, etc. Dynamic data collection offers me more relevant content as I navigate through the brand’s site. Any customer activities are recorded privately and securely over time so that once a customer is identified, the information is connected automatically.

Simply put, SAS Customer Intelligence 360 offers marketers the confidence to manage their digital customer journeys in a more personalized and profitable way. Marketers gain a complete view of their customers and transform this data using analytical insight into customer-centric knowledge and future actions. With this solution, brands can interact with customers on a personalized level and customers will be more satisfied with their entire relationship with a brand, not just a single transaction. Customer loyalty goes up and attrition goes down.

And as for me, I got the lens I was looking for, and was satisfied with the customer experience. Of course I have ideas on how to improve it on behalf of this brand, and SAS Customer Intelligence 360 fits into that picture.

tags: customer decision hub, customer journey, data hub, data scientist, Digital Intelligence, Predictive Marketing, Predictive Personalization, SAS Customer Intelligence 360

SAS Customer Intelligence 360: Digital discovery and engagement brought into focus was published on Customer Intelligence.

4月 202016
 

With more and more data available these days, and computers that can analyze that data, it's becoming feasible to look for fraud in events such as the Boston Marathon. So put on your detective hat, and follow along as I show you how to use SAS to be a data sleuth! […]

The post Looking for cheaters in the Boston Marathon data appeared first on SAS Learning Post.

4月 192016
 

Many companies are sitting on a goldmine: their data. But they may have no idea of its value.Companies that are not already thinking about analytics as the next logical step to harvest value and insights from their data need to rethink their strategy. They are in a way, very similar […]

Sleeping on a pile of gold? was published on SAS Voices.

4月 152016
 

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. Before launching the video, let me set some context for what you are about to see.

Why do we care about the intersection of digital analytics and personalization? Honestly, it is increasingly important to predict how customers will behave so you can personalize experiences with relevance. The deeper your understanding of customer behavior and lifestyle preferences, the more impactful personalization can be. 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. 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.

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. Most are based on simple rules-based recommendations, segmentation and targeting that are usually limited to a single customer touch point. Predictive MarketingDespite 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. Marketers are very creative, and can imagine hundreds of different testing ideas – which tests do we prioritize if we cannot run them all? 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.

Moving on to our second subject of interest, we transition to the wonderful world of marketing attribution. 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. The challenge is to uncover the interactions that drive conversions through integrated measurement and insights. However, organizations struggle to employ a holistic measurement approach because:

  1. It's confusing to distinguish among the measurement approaches available.
  2. Marketers bombard customers with extraneous content.
  3. Today's misaligned data makes customer level measurement a very difficult task.

It seems like attribution has been a problem for marketers for a very long time. According to a popular quote by Avinash Kaushik of Google:

“There are few things more complicated in analytics (all analytics, big data and huge data!) than multichannel attribution modeling."

The question is: Why is it challenging? SAS strongly believes three years later that we are living in a game-changing moment within digital analytics. 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. 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. The time has arrived for algorithmic attribution . . . Attribution

 

Did I whet your appetite? I hope so...please enjoy episode two of our two-part webcast series, now available for on demand viewing:

 

SAS for Digital Analytics: Personalization and Attribution [Part 2]

 

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. 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.

If you enjoyed this article, be sure to check out my other work here. Lastly, if you would like to connect on social media, link with me on Twitter or LinkedIn.

tags: Advanced Analytics, customer intelligence, Data Mining, data science, Digital Analytics, Digital Attribution, Digital Intelligence, digital marketing, Digital Personalization, marketing analytics, Predictive Marketing, Predictive Personalization, segmentation

Introduction to SAS for digital personalization and attribution was published on Customer Intelligence.

4月 142016
 

Across the globe, governments are losing billions in revenues to organised VAT fraud. The most recent VAT Gap study published by the European Commission estimates that EU countries lost an estimated €168 billion in VAT revenues in 2013. That's equivalent to 15.2% of the total expected VAT revenue from the […]

Attention VAT fraudsters: You’re no match for analytics! was published on SAS Voices.