Category Archives: Business Intelligence
This Datacamp infographic compares the popular programming languages for statistical analysis – SPSS, SAS, and R. As more data is issued via APIs and databases, organizations are turning to one or more programming languages. As Datacamp has noted on its blog, a “language war” is underway, with statisticians and programmers debating the merits of their favorite language. This comparison explains well the differences, though there are variations and nuances depending on the purpose of the language. R has been widely adopted because of its open source status, but SAS is supported throughout many industries. But in many cases, programmers and statisticians are using one or more languages.
You can learn about SAS through websites such as All Analytics. R Programming is covered at R-Blog. In the meantime, click on the infographic below to view how SAS, SPSS, and R differ…and compare favorably against each other.
Marketing automation is worth more than a process that advances analytics capability. It has a growing important value in a business strategic plan. Businesses struggle to organize their marketing, typically due to running separate social media, email, and platforms. The effort yields individual results to each platform, but can overlook multichannel opportunities or personalization which more customers crave.
Marketing automation addresses that need by consolidates marketing planning and reduces “clutter” from managing separate media. Planning how to automate can highlight where a message may not need to be repeated as well as how to repeat other messages that are valuable for the customer. The planning with marketing automation as a core function saves time and, when done right, improve marketing results.
Automation has a particular value for small businesses. Small business owners and their employees are busy, leaving a limited time to analyze analytics reports repeated. Marketing automation can streamline resources by automating marketing tasks.
There are three tips small and medium sized businesses can follow to prepare for adding marketing automation features.
- Get a lay of the initial data on the land. Establish the best data possible in the systems that will match up to your analytics. This means eliminating duplicate entries in sources such as CRM systems. Use advanced databases tools where possible to find consistent duplicates and errors.
- Roadmap how data will flow through the organization. Roadmap how an automation program will be implemented. Addressing all digital platforms at once can overlook needed steps. Set a six-month goal for full implementation, with milestones along the way. Use features like an annotation in Google Analytics to journal technical changes – some marketing decisions will trigger other analytics-related decisions such as adjusting tags, setting up remarketing campaigns for certain site visitors, changing filters, or adding custom variables.
- Align your sales and marketing teams to sync promotion communication. Plan the marketing and supporting automation system based on the buying cycle and lead nurturing stages. The sales team’s insight can ensure that your marketing efforts to brand and convert potential customers align with the sales team’s capabilities to execute. This can also indicate how alerts in analytics solutions should be distributed to the teams involved.
To be successful in business these days, small and medium businesses must become students of applying transparency at the right points within a business digital strategy. This has been due to customers increasingly using online search and social media to determine a purchase decision. Thus businesses can examine analytics data to reveal how its digital transparency online influences that sales decision.
Influence has been emerging as an crucial measurement to be monitored. It affects the approach a business takes towards meaningful analytics strategy and igniting ongoing debate about how to define digital influence from a post or ad.
Do you think influence online is overemphasized? You may want to consider these changes as a signal of influence’s value:
- Google’s search engine update in November 2011 heralded site content recency or “freshness” as a significant algorithm factor. Subsequent algorithms, such as Hummingbird, introduced in 2013, extended the emphasis on content relevancy to search queries.
- Improvements to Google Analytics reports include social plug-in, which measures the reach of tagged pages shared through Google Plus, Facebook and Twitter platforms, and multichannel reporting, which indicates which channel influences a conversion.
- Enterprise level analytic solutions incorporating API-based data into their reports, permitting more dashboard and visualization options.
Influence is becoming a great strategic value, drawing businesses to increase their reliance on social media and digital marketing tools. Forrester has also noted in its report US Interactive Marketing Forecast that digital marketers expect to spend $4.4 billion by 2016. That spend indicates investment in sending a message in front of an audience. Furthermore, this burgeoning interest reinforces the benefits analytics brings to business, and drive further overall interest in analytics beyond discussion of website technicalities.
A business desiring to measure influence must also provide proof that validates customers’ interest in them. That means being transparent with operations that are relevant with a potential customer. People want to learn more about whom they are doing business with online. Badges and rating have thus gained mindshare of website visitors. The end result is visitor who may repeat a visit. Increased repetition of visits can turn a fence-sitting customer into an actual customer.
Rajeev Malik, co-founder of KikScore, an online rating system bought by Google in 2012, references his start up as an example of how influential online confidence badging can be through the transparency of its rating system. KikScore offered a rating system that displayed a confidence badge of key policies and services displays credibility information for a small business. The score was meant to raise the trustworthiness of a business, increasing the likelihood of converting website visitors into sales or leads.
In an email exchange with me, Malik showed me the key information that visitors look for in transparency.
- Management Information: Displaying the owners and management team behind a business and website gives shoppers an idea of who they are dealing with.
- Financial History: Baseline non-confidential financial stability and track records of strong financial stability help comfort shoppers that the business and website that they are visiting is legitimate and not a fraudster/scammer.
- Location Information: Offering website visitors and shoppers information about locations for your business can remind visitors and shoppers that you are convenient to their travels.
- Website Information: To the extent that small businesses can provide information about their infrastructure, security and hosting information that indicates website security and that there is a legitimate person that is operating the website.
- Policy Information: Providing details and specifics, such as deliveries, returns, customer service, privacy and other types of related policies helps shoppers get an idea about expectations in the event that a purchase is not delivered or a service is not rendered.
- Certifications and Awards: Instead of hanging awards up in their own offices, small businesses should mention their honors on the business website. Third party certifications and awards provide confirmation that others believe your website and business is credible and trustworthy.
As stated earlier, there will be much debate about what influence measurement truly means and how it connects to business decisions. Ultimately the importance of transparent rating systems and influence online can not be underestimated. Managing a Klout score, for example, has gained traction – and rancorous debate – among marketing critics.
Analytics is rooted in Greek language. The word means “breakdown”. What we choose to breakdown has changed from what we know a few years ago, and we need to continue our vigilance in developing the right message in our digital marketing that reflect transparent business values.
The video below was created for UBM Tech’s site Business Agility (sponsored by IBM). The site, a perspective on business technology that analysts and managers face, went dark about a year ago, but much of the material remains relevant.
This video examines the importance of analytics, but from a perspective of business structure. Analytics impacts how your business operates. The iterative nature of analytics creates an opportunity to manage resources and tools. These operations can then be optimized for growing a business vs. mere acquiring a business. Looking at the leaders in retail and technology give evidence that analytics, when launched properly, can provide the right boost for a business.
What do think managers should focus upon to strengthen their tool set? Share your thoughts here on Zimana blog.
In the rush to learn big data, programming languages are becoming more central to solid analysis. The most popular so far has only one letter: R.
R is an open source language used for developing correlations. It is typically used with big data analysis on semi-structured data such as product reviews, images, and likes. It is powering the heart of statistics modeling, programming, and data visualization.
Because big data interest has skyrocketed, so has the interest and usage for R. A Data Informed article noted that in a survey data scientists 70% of those surveyed are using R alongside other programming languages. In fact some applications include R into their interface for more cohesive analysis.
For those professionals still learning, the good news is that there are several resources available for R programming. Here are a few resources for those who want to get acquainted with R:
R-bloggers: This blog covers a number of R programming concerns and related topics. The best aspect about the site is a community with varying aspects of the R programming language – Over 450 contributors offer their insights and best practice techniques. A job posting for R-related positions also appears on the site. Check out more at the R-bloggers site.
Deducer: Touted as an SPSS alternative, this open source interface is designed for professionals who do not have a full background in data science. The GUI is free to download at the Deducer site.
R-studio: Another free open source GUI available, R-studio is designed to make programming in R more user friendly and easier to edit.
Datacamp: Datacamp is similar to a number of online course sites – Udemy, Udacity, +Tuts – yet it focuses exclusively on data science. Datacamp provides free training on R as well as a general overview of what a data scientist is. Additional resources are also available at the Datacamp site.
Data Science Central is a great site that offers insights into current data concerns such as Hadoop and data visualization. The site offers a comprehensive summary of initial data analysis techniques using R in the page Summary of R via Data Science Central. Data for some to the explanations are also available via a zip file on the page.
There is an online book for more advance techniques called, strangely enough, Advanced R by Hudly Wickham. The site provides an overview of data structure and functional programming, with a special emphasis of package development.