customer intelligence

11月 092018
 

In parts one and two of this blog posting series, we introduced machine learning models and the complexity that comes along with their extraordinary predictive abilities. Following this, we defined interpretability within machine learning, made the case for why we need it, and where it applies. In part three of [...]

SAS Customer Intelligence 360: A look inside the black box of machine learning [Part 3] was published on Customer Intelligence Blog.

11月 092018
 

In parts one and two of this blog posting series, we introduced machine learning models and the complexity that comes along with their extraordinary predictive abilities. Following this, we defined interpretability within machine learning, made the case for why we need it, and where it applies. In part three of [...]

SAS Customer Intelligence 360: A look inside the black box of machine learning [Part 3] was published on Customer Intelligence Blog.

11月 062018
 

The only constant is change – Heraclitus As online user behavior continues to evolve, user expectations are growing as well. More and more, users expect to be known; thus, web personalization and targeted content are becoming mission critical and an expectation, not an exception. Using SAS Customer Intelligence 360, we [...]

Using SAS at SAS: How content targeting drives better UX was published on Customer Intelligence Blog.

11月 052018
 

In part one of this blog posting series, we introduced machine learning models as a multifaceted and evolving topic. The complexity that gives extraordinary predictive abilities also makes these models challenging to understand. They generally don’t provide a clear explanation, and brands experimenting with machine learning are questioning whether they [...]

SAS Customer Intelligence 360: A look inside the black box of machine learning [Part 2] was published on Customer Intelligence Blog.

11月 012018
 

As machine learning takes its place in numerous advances within the marketing ecosystem, the interpretability of these modernized algorithmic approaches grows in importance. According to my SAS peer Ilknur Kaynar Kabul: We are surrounded with applications powered by machine learning, and we’re personally affected by the decisions made by machines [...]

SAS Customer Intelligence 360: A look inside the black box of machine learning [Part 1] was published on Customer Intelligence Blog.

8月 022018
 

Recently, Scott Jackson, Director Business Intelligence at the University of North Carolina at Chapel Hill shared their data quality, reporting and analytics journey. They're using SAS in a multitude of ways – from operations, institutional research, athletics – and are now looking to scale to the enterprise. They've been so successful [...]

Scaling data and analytics across the University of North Carolina was published on SAS Voices by Georgia Mariani

7月 132018
 

In part one of this blog posting series, we took an introductory tour of recommendation systems, digital marketing, and SAS Customer Intelligence 360. Helping users of your website or mobile properties find items of interest is useful in almost any situation. This is why the concept of personalized marketing is [...]

SAS Customer Intelligence 360: The Digital Shapeshifter of Recommendation Systems [Part 2] was published on Customer Intelligence Blog.

6月 132018
 

What do the New York Mets, the Orlando Magic and the Boston Bruins all have in common? They all use SAS analytics to gain deeper insights into athlete recruitment, retention, performance, safety and more. And after seeing the success teams like these have had using analytics, collegiate sports are turning [...]

The key to success in college sports? Analytics. was published on SAS Voices by Georgia Mariani

5月 182018
 

When data meets geography, use cases revolve around mapping and spatial analytics. But what happens when you combine digital analytics and powerful visualization for customer location analysis? Leveraging data collection mechanisms, SAS 360 Discover captures first-party behavioral information across the entire digital customer experience with a brand’s websites and mobile [...]

SAS Customer Intelligence 360: Location analytics meets digital intelligence was published on Customer Intelligence Blog.

4月 052018
 

SAS Global Forum 2018 takes place April 8-11 in Denver. The following post is from Sebastian Dziadkowiec and Piotr Czetwertynski, presenters at the event. You can join Sebastian and Piotr for their talk: “An Agile Approach to Building an Omni-Channel Customer Experience” on April 9 at 2 p.m. in Meeting Room 302. We'll also post their presentation here after the event has concluded.

Keys to building a successful and future-proof omni-channel customer experience

Most organizations acknowledge that building a seamless and consistent customer experience is critical to long-term success. The big question is: Now what? With all of the channels to stitch together – from brick and mortar experiences to online clicks – how do you track and make sense of all that customer data? And, more importantly, how do you use that data to create the very best customer experience?

Over many years of implementing SAS Customer Intelligence and helping our clients give their customers exactly what they want and when they want it, our team has identified some characteristics that make for successful projects. Here are some of the key components that most often make or break a Customer Intelligence project.

Time to market

Everyone likes to see value generated quickly and reaching the break-even point for project within weeks of project launch is critical. In case of campaign management, it is possible. Instead of following the traditional waterfall path, with all the IT-heavy components like requirements gathering and analysis, solution design, many streams of implementation and testing, it is worth considering releasing a minimum viable product as soon as possible. Such approach allows us to focus on delivering business value and field-testing all the creative ideas, rather than building an IT system in perfect accordance to requirements, and one that may no longer be relevant at the day of release.

Applying analytics in the decisioning process

Go beyond traditional, rule-based approach to get the most out of the data you have. Nowadays, everyone speaks about machine learning, big data, NBA, artificial intelligence and so on. It is up to each organization and CI project to forge those fancy buzz words into real value, by embedding advanced analytics techniques in the decisioning process. There are many ways to boost various use cases by the advanced methods; make sure you will be able to use all you need and integrate their results seamlessly, regardless of when and how you engage with your customers.

While working on a CI project you should also keep in mind other areas: project organization, building a future-proof solution that will stay relevant for years, and constant search for additional opportunities to use available data and solutions to generate incremental value beyond the core scope of customer intelligence project.

There isn’t a one-size fits all approach to implementing a CI project, but these lessons learned can greatly increase your chances for project success – successful delivery generating a high ROI in a short timeframe while staying relevant in the long run - through the very best possible customer experience.

Find out more at the SAS Global User Forum 2018

Join Sebastian and Piotr for their “An Agile Approach to Building an Omni-Channel Customer Experience” Breakout Session at SAS Global Forum April 9 at 2 p.m. in Meeting Room 302.

About the Authors

Piotr Czetwertyński

Piotr is Customer Analytics Manager in Accenture. He has 11 years of experience in Campaign Management and Analytics. Currently he is one of the people responsible for launching of Accenture Center of Excellence for SAS CI in Warsaw, Poland.

Piotr recently focuses on solutioning & strategy in the areas of campaign management, BI & Analytics.

Sebastian Dziadkowiec

Sebastian has 8 years of experience in technology and management consulting, mostly in communications industry. He went through the entire project lifecycle on numerous engagements, starting from programmer, through business and technical analyst, up to solution architect and team manager on large-scale analytics projects.

Sebastian specializes in analytics solutions technology architecture, particularly focusing on customer intelligence and big data. He serves as technology lead in Accenture Center of Excellence for SAS CI in Warsaw, Poland.

 

 

Keys to building a successful and future-proof omni-channel customer experience was published on SAS Users.