Semphonic – Digital Analytics - Thought Leadership

Clearsight Advises Semphonic in Acquisition by Ernst & Young

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But technology sometimes like religion. You have to find out what people are most comfortable with. At Marketo, it was Slack.

At Salesforce, it is Chatter. For me, I prefer to be Skyped!. I was always the kid who liked to sit in the very back of the class hunched down behind somebody who played Right Guard on the football team. That being said, I have some issues with chat too. But I always viewed serious code writing as essentially monastic.

Can Digital Analytics, Marketing, IT and BI Get Along in the World of Big Data?

That may seem ludicrous, but writing large scale software is a real intellectual undertaking Eighteen years ago I stepped outside my comfortable door and was swept out into a Wilder since the early days of Web Analytics. So I thought a conversation on the pitfalls and challenges might be interesting and useful. When you read white p In my last post I described some of the biggest challenges to a traditional enterprise trying to drive digital transformation.

The need for customer-centricity penalizes organizations setup in careful siloes.

And these very real hurdles are exacerbated by the way digital often creates poor decision-making in otherwise skilled organizations because of what I termed the reverse hierarchy of understanding. The reverse hierarchy of understanding is a pretty simple concept. Organizations work best when the most senior folks know the most about the business. When, in other words, knowledge and seniority track.

For the most part and despite a penchant for folks lower down in the organization to always think otherw Why is it so hard for the traditional enterprise to do digital well? When digital is in your DNA it seems perfectly manageable. Of course, mastering any complex and competitive field is going to be a challenge. But for companies born into digital, doing it well is just the age-old challenge of doing ANY business well.

For most traditional enterprises, however, digital has been consistently hard. So what is it that makes digital a particular challenge for the traditional enterprise?

Digital metrics & personalisation: What’s next?

That was the topic of my last conversational session at the Digital Analytics Hub this past week in Monterey and if you didn't go And with a group that included analytics Burning Down the HouseNowhere is the challenge of getting people to understand how to use data better illustrated than the methodology wars being fought in the discipline of Psychology. A ridiculous percentage of business books seem to me either to be one-trick ponies a good idea that could be expressed fully in a magazi We covered a lot of interesting ground, but organizing digital in the enterprise was the most challenging part of that discussion.

So in this p Competitive Advantage and Digital TransformationIn my last posts before the DA Hub, I described the first two parts of an analytics driven digital transformation. The first part covered the foundational activities that help an organization understand digital and think and decide ab And while I love talking analytics, thank heavens I had a few opportunities to get awa Of course, doing these Digital Transformation at the enterprise level is hard.

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As a consequence, web analytics tools provide little or no access to detailed data, little or no customer level analysis, and little or no ability to do advanced analytics or customer-based testing. Though most web analytics tools upgraded their data platforms in to support increased segmentation, these changes still leave many with limited customer analytics capabilities.

Organisations have rapidly started to investigate either internal or cloud-based warehousing solutions that provide much deeper access and integration of the data. That means unlimited tables, unlimited fields, multiple data types, flexible access paths, unlimited data transformation, and open tool access. This makes it much easier to drive outbound services and if your organisation is interested in actually using Web data to drive personalisation, targeting, or CRM support, this outbound capability is critical.

The single most important thing to know about a customer is what they are doing right now. Black-box solutions are nowhere near as capable of building meaning out of data as can be done by a good analyst with the correct tools. But real-time micro-decisioning is hard even on very fast systems. You have to assemble, analyse, and act on the data in sub-second timeframes.

Because of this, many organisations forgo real-time decision-making — leaving the biggest analytics ROI on the table.

What makes a great Loyalty Marketing professional?

Getting data into a warehouse and exposing it to powerful query and analysis tools sounds like a panacea for all the problems associated with using web data. Between big-data engines and powerful analysis tools, organisations will surely be able to milk this data of its full value. In digital analytics, two huge challenges face any technology designed to support digital marketing analytics.

Nearly all current digital data warehouses rely on data feeds from existing tagging systems. Not only do these systems carry over the problems that have brought tagging to a crisis point, but they introduce severe delays into the system. Tempting as they may seem, a web analytics tool is the wrong way to source your analytics warehouse.

Analytics Leadership via Cross Departmental Team

Systems dedicated to providing real-time sourcing of web data to the warehouse are starting to emerge, and organisations seeking to create a robust infrastructure for the warehouse that extends beyond the next 12 months must now look beyond their web analytics tool to an infrastructure specifically designed for the task. Equally problematic is the question of how to understand digital data. Proper web analytics data — how visitors move from page to page on a website — is rare for most marketers.

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