Interested in slicing, dicing, measuring, and analyzing data for customer and business insights?
According to a recent survey by Bloomberg, 97% of companies with revenues of more than $100 million are using some form of business analytics, up from 90% just two years ago.
While businesses have embraced the idea of fact-based decision-making, a steep learning curve remains. Only one in four organizations believes its use of business analytics has been “very effective” in helping to make decisions. Data is not just ignored but often discarded in many organizations as the business users can’t figure out how to extract signal from data noise.
Machine data or “data exhaust” analysis is one of the fastest growing segments of “big data”–generated by websites, applications, servers, networks, mobile devices and other sources. The goal is to aggregate, parse and visualize this data – log files, scripts, messages, alerts, changes, IT configurations, tickets, user profiles etc – to spot trends and act.
By monitoring and analyzing data from customer clickstreams, transactions, log files to network activity and call records–and more, there is new breed of startups that are racing to convert “invisible” machine data into useful performance insights. The label for this type of analytics – operational or application performance intelligence.
In this posting we cover a low profile big data company, Splunk which recently went public. Splunk has >3500 customers already. Splunk ended its first day on the stock market with amazing 108.7 percent bump in price from its $17-per-share IPO.
Next best offer, next best action, interaction optimization, and experience optimization typically have similar architecture. Machine learning and multivariate statistical analysis are at the heart of these cutting edge Behavioral Analytics strategies. Typically firms use statistical tools for segmentation models, behavioral propensity modeling, and market basket analysis.
The bleeding edge in next best offer is increasingly around:
- Applying machine learning to find connections between product tastes and different affinity statements
- Developing low-latency algorithms that help show the right product at the right time to a customer
- Developing rich customer affinity profiles through a variety of feedback loops as well as third-party data source (e.g. Facebook user demos and taste graph)
Targeted Offer Solutions
Do you have the right toolset, dataset, skillset and mindset for analytics? Do you want to enable end users to get access to their data without having to go through intermediaries?
The challenge facing managers in every industry is not trivial… how do you effectively derive insights from the deluge of data? How do you structure and execute analytics programs (Infrastructure + Applications + Business Insights) with limited budgets?
Procurement organizations tend to swim in data. One of the most important strategies for any best-in class procurement organization is spend analytics. In conjunction with sourcing, category, contract management and purchasing, spend analytics provides a window into spend behavior to drive cost reduction and cost avoidance efforts.
As a result, we are seeing a lot of interest around Spend BI and Analytics projects. Chief Procurement Officers and other Sourcing/Procurement leaders of Global, large and even mid-market firms are increasingly focusing on spend data analytics as part of a new wave of spend rationalization projects. Read more
Best-in-Class Behavioral Analytics Case Study…I am reposting this well written article by Charles Duhigg on how Target is targeting customers using Predictive Analytics to anticipate shopper behavior.
Target was founded in 1902 and is headquartered in Minneapolis, Minnesota. Target operates over 1,750 stores in 49 states under Target and SuperTarget names. It offers general merchandise products through its Website, Target.com. The company distributes its merchandise through a network of distribution centers, as well as third parties and direct shipping. Additionally, it offers credit to guests through its branded proprietary credit cards.
Data Analytics and Influencing Pregnant Shoppers
Andrew Pole had just started working as a statistician for Target in 2002, when two colleagues from the marketing department stopped by his desk to ask an odd question: “If we wanted to figure out if a customer is pregnant, even if she didn’t want us to know, can you do that? ”
As the marketers explained to Pole new parents are a retailer’s holy grail. Most shoppers don’t buy everything they need at one store. Instead, they buy groceries at the grocery store and toys at the toy store, and they visit Target only when they need certain items they associate with Target — cleaning supplies, say, or new socks or a six-month supply of toilet paper. But Target sells everything from milk to stuffed animals to lawn furniture to electronics, so one of the company’s primary goals is convincing customers that the only store they need is Target. But it’s a tough message to get across, even with the most ingenious ad campaigns, because once consumers’ shopping habits are ingrained, it’s incredibly difficult to change them. Read more
P&G OverviewP&G’s has 127,000 employees and 300 brands sold in 180 countries. P&G averages about 4 billion transactions daily. P&G CEO Bob McDonald has staked out a strategy to “digitize” the company’s processes from end to end, and Business Sufficiency, Business Sphere and Decision Cockpits is enabler of that agenda. P&G is building analytics expertise at a time when P&G is cutting costs in other areas, including eliminating 1,600 nonmanufacturing jobs. The company’s IT organization itself has cut $900 million in total spending over the past nine years. P&G is investing in analytics talent, even as the company cuts in other areas, to speed up business decision making. True leaders develop the capabilities required for making good and timely decisions in unpredictable and stressful environments. Read more
The exploding demand for analytics professionals has exceeded all expectations, and is driven by the Big Data tidal wave. Big data is a term commonly applied to large data sets where volume, variety, velocity, or multi-structured data complexity are beyond the ability of commonly used software tools to efficiently capture, manage, and process.
To get value from big data, ‘quants’ or data scientists are becoming analytic innovators who create tremendous business value within an organization, quickly exploring and uncovering game-changing insights from vast volumes of data, as opposed to merely accessing transactional data for operational reporting.
This EMC infographic summarizing their Data Scientist study supports my hypothesis – Data is becoming new oil and we need a new category of professionals to handle the downstream and upstream aspects of drilling, refining and distribution. Data is one of the most valuable assets within an organization. With business process automation, the amount of data being generated, stored and analyzed by organizations is exploding.
Following up on our previous blog post – Are you one of these — Data Scientist, Analytics Guru, Math Geek or Quant Jock? – I am convinced that future jobs are going to be centered around “Raw Data -> Aggregate Data -> Intelligence ->Insight -> Decisions” data chain. We are simply industrializing the chain as machines/automation takes over the lower end of the spectrum. Also Web 2.0 and Social Media are creating an interesting data feedback loop – users contribute to the products they use via likes, comments, etc.
CIOs are faced with the daunting task of unlocking the value of their data efficiently in the time-frame required to make accurate decisions. To support the CIOs, companies like IBM are attempting to become a one-stop shop by a rapid-fire $14 Bln plus acquisition strategy: Cognos, Netezza, SPSS, ILog, Solid, CoreMetrics, Algorithmics, Unica, Datacap, OpenPages, Clarity Systems, Emptoris, DemandTec (for retail). IBM also has other information management assets like Ascential, Filenet, Watson, DB2 etc. They are building a formidable ecosystem around data. They see this as a $20Bln per year opportunity in managing the data, understanding the data and then acting on the data. Read more
In 1999, multichannel retailer Circuit City pioneered the option to buy a product online and pick it up in-store.
Today, as digital and mobile channels accelerate in terms of adoption retailers like Best Buy, Lowes, Barnes and Noble, Saks, Macys, Nordstrom are very worried about becoming showrooms for online retailers.
This “showrooming” trend is a seismic shift and already posing a strategic problem in consumer electronics, books, shoes, appliances where a growing number of consumers are going to retailers to test drive products and then go online with mobile phones to transact at a cheaper price elsewhere. Best Buy and others appear to be in danger of turning into Amazon.com’s “showroom”, which has the advantage of lower overhead costs and mostly can avoid sales tax collection.
To create shopper stickiness they are trying parallel strategies (1) offering the buy online, pick up in-store or ship-to-store options; (2) price match; (3) same-day delivery options. Amazon.com, on the other side, is playing offense by partnering with high brick-and-mortar footprint companies including Staples, RadioShack, and 7-Eleven, to place Amazon delivery lockers in hundreds of stores across the country.