Martech enablement series: Part 7 — Insights, intelligence and integration

Welcome to Part 7 of: “A Nine Part Practical Guide to Martech Enablement.” This is a progressive guide, with each part building on the previous sections and focused on outlining a process to build a data-driven, technology-driven marketing organization within your company. Below is a list of the previous articles for your reference:

  • Part 1: What is Martech Enablement?
  • Part 2: The Race Team Analogy
  • Part 3: The Team Members
  • Part 4: Building the Team
  • Part 5: The Team Strategy
  • Part 6: Building the Car

In these previous parts, we looked at how your martech team is parallel to an automobile race team. We spent time investigating how a race team constructs their team and then builds a strategy for winning their individual races and the overall race series. We then looked at how this is also a successful approach to constructing and strategizing for a martech team, identifying this process as “martech enablement.”

As we discussed in Part 1 of this guide, martech enablement is ultimately about obtaining insights and providing tools and processes to take action to affect your marketing efforts in your marketing organization. In Part 6, we discussed “building the car” with a focus on breaking down the systems in your martech stack that allow you to take action.

In this article, we will explore the systems that provide insights and enable team collaboration. We’ll also look at tying them all together with integration approaches, tools and strategies. Once again, a shout-out to Scott Brinker for producing the “Marketing Technology Landscape” to help make sense of all the martech products available.

Insights and intelligence

When you’re driving your car, a number of tools inform you how to take action. Looking out your windshield, windows and mirrors gives you immediate data that you respond to. Additionally, you have tools like your instrument dashboard, GPS, traffic data, your radio, and even your passengers.

Race drivers and the team as a whole have sophisticated systems in and around the car that are collecting information, as well as experts to analyze the information in real time, providing actionable insights that the team can use before, during and after the race. This is a huge part of the team’s competitive advantage that they use to win races.

Part of the martech enablement process is to leverage the data within your martech stack so that experts within your team can analyze that information to provide actionable insights, so your marketing organization can win your race.

To reiterate a point made in Part 6 of this guide, a solid data strategy is one of the most important components of martech enablement. This provides the foundation for extracting and “mashing” this data in a way that you can measure. A sound approach is to understand your organization’s KPIs (key performance indicators) and craft a data strategy that supports collecting data to enable measurement of those KPIs.

Many systems and categories of tools assist in the area of gaining insights. Below is a list of some of the systems used to provide visibility and understanding:

  • Web analytics platforms
  • AI/predictive analytics
  • MPM — Marketing performance management
  • Marketing attribution systems
  • Business intelligence (BI) systems
  • Dashboards
  • Data visualization tools
  • Social media monitoring
  • Sales intelligence
  • Audience and market research data

As you progress through the martech enablement process, your “insights” toolset will grow in both size and maturity. I want to remind you to stay focused on letting this part of your stack evolve from the incremental team objectives and series and race goals. Don’t lead with a goal of creating a cool BI environment or dashboard. Let these grow out of the goals driving the martech enablement process.

Strategic vs. tactical insights

I want to spend a minute discussing the difference between strategic and tactical insights and their alignment with your team, series and race objectives. For a refresher on these, see Part 5 of this guide.

When measuring and analyzing performance against your team and series goals, you’re looking at strategic insights where understanding the current level and performance trend is desirable. Think in terms of tools that show you the results of your marketing efforts across time. A tactical insight will generally be more closely aligned with your race goals and will be a singular value or KPI.

Relating this to our race team analogy, a strategic goal could be wanting to improve the team’s average finish position from the current state to some future targeted goal. Over time, you could measure and graph the improvement and trend toward that goal.

A tactical goal might be the desire to come in third place or better in a particular race. Your insight tool could represent that number as a single KPI. That isn’t to say that you may never analyze performance trends during a race, such as average lap speed. But there are values that benefit from analyzing as a trend and others that are just fine to analyze as a current and ending value.

Team management and collaboration

When it comes to management and collaboration in the race team, both pre-race and race-day systems are needed to support the team’s operations. These tools are necessary to get things done right in your marketing organization. Good management and collaboration tools help great people be a great team. Here are some of those systems:

  • Project management
  • Workflow
  • Collaboration tools
  • Business Process Management (BPM)/Agile & Lean
  • Talent management
  • Vendor management
  • Budget and finance

The nuts, bolts, welds, hoses and wires

It’s important to have a strategy and tools to hold all of this together. There are a few strategies to contemplate with systems integration and martech. Your marketing organization will likely take several different approaches to integration. These are generally broken down into three categories: native integration, IPaaS (integration platform as a service) and custom integration.

As technology matures, and the interoperability of products grows, companies are building “connectors” that allow for the exchange of data between their products and other widely used ones. These native integrations generally require some technical implementation or configuration, but the product manufacturers have done much of the heavy lifting to allow for the exchange of data between systems they have connectors for.

IPaaS is a “suite of cloud services enabling development, execution and governance of integration flows connecting any combination of on-premises and cloud-based processes, services, applications and data within individual or across multiple organizations,” according to Gartner. These platforms enable a more systematic way of creating and controlling data exchanges between products in your martech stack.

Custom development is as it sounds: a process in which software engineers develop custom applications to create and manage data exchanges between products and systems in your martech stack. Regardless of whether you take advantage of the aforementioned native integrations or IPaaS, you will likely at some level need to leverage good technologists to do some custom integration work along your path to martech enablement.

Stack it up!

To review, all the categories of the stack between Part 6, “Building the car,” and this part, “Supporting technologies,” your cohesive martech stack is composed of the following types of systems:

Intro to Part 8: Running the series and the races

Now that we’ve gone through the people, the strategy and the stack, we can move on to the execution part of martech enablement. In Part 8 of the guide, we’ll get into how your team iteratively and incrementally moves your marketing organization toward digital transformation and maturity.

I look forward to continuing to share with you about martech enablement in Part 8 of this guide.


Some opinions expressed in this article may be those of a guest author and not necessarily MarTech Today. Staff authors are listed here.


[“Source-martechtoday”]

Getting Better Analytics And Insights From Your Collected Customer Data

Data collection and analytics are tightly coupled. The mistake we see made over and over again is that companies tend to focus their customer data collection efforts with a single objective (or a single program) in mind. This treats the data collected as a short-term objective, not as a long-term asset. Over time, this results in data islands that eventually “go dark” given that no one is managing customer data as part of an explicit long-term effort.

Have A Long-Term Data Strategy

When it comes to customer data, a long-term data collection strategy almost always proves critical for any advanced analytical work that leads to meaningful business outcomes that can optimize (i.e., simulation management, condition-based maintenance, predictive maintenance and digital twins). Trending analysis, predicting behavior and customer profiling all benefit from long-term data collection strategies. Companies that understand customers’ buying patterns over longer time frames stand to win key insights versus their competitors.

Customer data deserves a data-access-centric strategy to ensure that the data is treated as a reusable asset. This implies that the data should be available to the right people in the company when they need to repurpose it or mine it months or years later. If the data is not findable, threadable (tied to other data sets) or readily accessible, then it’s effectively dark, and its chances of being repurposed are low.

If you are storing your customer data like you store everything else, chances are much of the data you’ve collected from customers has already gone dark. The tendency is to focus on analytical outcomes without preparing the precondition required for the analytics to occur over a longer period of time. If a data strategy for customer information isn’t well-executed, then customer data will reflect the problem you already have in your data center — lots and lots of data sets that represent difficult-to-access data islands.

Thread Your Data

Sophisticated analytical efforts require advanced techniques such as data threading. Threading data across many silos of data is a challenging undertaking. Techniques deployed to achieve threading include (re)ingestion of data, aggregation, parsing, meta data enrichment and indexing. Data is often so extremely siloed that the most efficient first step is simply discovering data islands and recollecting them into an architecture that allows for advanced analytics. The good news is that data capture and storage technologies are relatively cheap, but finding data and then curating it properly does require significant investment.

Customer data needs to be curated and managed as an asset. As more data is collected, it needs to be aggregated with customer data collected during the previous year (or the last campaign, the last payables cycle, etc.).

For example, if a financial institution wants to understand if a customer is approaching a life-changing event such as marriage, having children or purchasing a home, then threading becomes important because it lets you piece together various customer data collection efforts into a single threaded digital dossier. The threaded customer digital dossier allows for different customer data (collected at different points in time) to be accessed for future analytics. It treats customer data as valuable, evergreen and interconnected. A data architecture that allows you to thread and incrementally expand the customer data set is an essential component to making more with your customer data. Advanced analytics, in turn, will allow you make better use of customer data that is properly curated through threading or other data access techniques.

Teamwork

Separating the customer data collection process from the data curation process from data analytics is not a recipe for success. Unfortunately, most companies treat these three activities independent of each other. As a result, customer data is underutilized, undervalued and is not curated as a long-term asset.

The best customer analytics happen when you intersect people who understand the customer data being collected with people who understand how to use and access the data over time. This means that customer data collection efforts need to be discussed in one room with data architecture folks, analytical/data science teams and traditional marketing/customer success teams, ensuring that all have an active voice at the table.

[“Source-forbes”]

Insights into atomic structure of next-generation superconductors

Insights into atomic structure of next-generation superconductors

Neutron diffraction at the Australian Centre for Neutron Scattering has clarified the absence of magnetic order and classified the superconductivity of a new next-generation of superconductors in a paper published in Europhysics Letters.

The iron-based nitride, ThFeAsN, which contains Th2N2 and FeAs2 layers, has been of considerable interest because unconventional superconductivity occurring at a temperature of 30 K. This material was of particular interest as the superconductivity was seen to arise without oxygen doping.

A large group of predominantly Chinese researchers, led by Prof Huiqian Luo from the Beijing National Laboratory for Condensed Matter Physics gathered diffraction measurements on the high intensity diffractometer WOMBAT, assisted by instrument scientists Dr Helen Maynard-Casely and Dr Guochu Deng based at the Australian Centre for Neutron Scattering. This enabled them to determine the crystal structure of the compound over a large temperature range.

In similar types of materials, the onset of a superconducting state is thought to be associated with magnetic ordering within the crystal structure. Earlier measurements had shown no magnetic ordering in the ThFeAsN material, and hence this neutron study was an opportunity to confirm this and search for other structural insights into the material’s properties.

The lack of magnetic order was confirmed because no difference was found between the data sets at 6 K and 40 K. All of the observed reflections could be could be identified as having arisen from the atomic structure from 6K up to 300K – no magnetic reflections were identified.

Diffraction patterns over the temperature range from 300 K to 6 K also indicated there was no structural phase transition from tetragonal to orthorhombic in the crystal lattice.

The investigators reported that the lattice parameters continuously increased with temperature due to thermal expansion and a weak distortion in the tetrahedron possibly took place at 160 K. Details from the structure point to this distortion coming from the FeAs2 layers.

The close relationship between local structure of the FeAs4 tetrahedron and the superconducting temperature, suggested TheFeAsN is in a nearly optimised superconducting state.

This is different to many other discovered superconducting materials, which require tweaks in their chemistry to produce the highest critical temperature.

The authors also surmised that the close distance of Fe-As would favour electron hopping, reducing electron correlations and orbital order, thereby providing a reasonable explanation for the absence of magnetic order, structural transition and resistivity anomaly.

Carrier density measurements indicated that ThFeAsN could already be doped by electrons, which are probably introduced by the N deficiency or O occupancy or the reduced valence of nitrogen. The self-doping effect could be responsible for the superconductivity and suppression of magnetic order.

 

[“Source-phys”]

Key Consumer Sector Insights

Market and consumer sector’s performance last week

The second quarter earnings season ended on a productive note. The S&P 500 Index (SPY) (SPX-INDEX) finished the week ending September 1 on a positive note with a 1.4% gain. Brown-Forman stock rose last week and benefited the consumer staples sector with its strong 1Q18 results. On the other hand, Campbell Soup stock (CPB) pulled down the staples sector. Its earnings and revenues missed its fiscal 4Q17 results. Overall, the S&P 500 Consumer Staples Index rose 0.51% last week.

Key Consumer Sector Insights for August 28–September 1, 2017

In the consumer discretionary sector, Best Buy (BBY), H&R Block (HRB), and Dollar General (DG) fell last week after their earnings results. However, automakers General Motors (GM) and Ford (F) rose. Their August sales results benefited the sector. The S&P 500 Consumer Discretionary Index rose 1.6% last week.

Other events last week that impacted the market included the US August jobs report on Friday. The United States Department of Labor said that the US economy added 156,000 jobs in August—lower than economists’ expectations of 180,000 jobs. The unemployment rate in the US rose to 4.4% from 4.3%. Average hourly wages rose 2.5% in the past 12 months. The disappointing jobs report might reduce the chances of another Fed rate hike this year. The jobs report had a subdued impact on the S&P 500 because automakers’ stock rose.

Consumer ETFs were productive last week. The Consumer Discretionary Select Sector SPDR Fund (XLY) rose 1.6% on a weekly basis—the highest among consumer ETFs. The SPDR S&P Retail ETF (XRT) rose 1.0% and the Consumer Staples Select Sector SPDR ETF (XLP) rose 0.55% last week.

Consumer Sector Overview: August 28–September 1, 2017 PART 2 OF 6

Analyzing the Consumer Sector’s Performance Last Week

Index performance last week

As of September 1, the S&P 500 Index (10.6%) (SPY) (SPX-Index) has outperformed the S&P 500 Consumer Discretionary Index (10.4%) (XLY) and the S&P 500 Consumer Staples Index (6.1%) (XLP) on a YTD (year-to-date) basis.

Analyzing the Consumer Sector’s Performance Last Week

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Key updates

On September 1, General Motors (GM) released its August sales report. In August, US retail sales recorded 275,552 vehicles—7.5% higher YoY (year-over-year). The company’s commercial sales have risen 19% YoY. General Motors gained domestic commercial market share for 13 consecutive months. Its commercial market share was driven by strong crossover sales at all four of the company’s brands. General Motors stock rose ~5.0% last week.

On September 1, Ford (F) released its sales results for August. The company’s overall sales fell 2.1% to 209,897 vehicles in August. It was mainly impacted by lower fleet sales, which fell 0.2%. Ford’s retail sales for August fell 2.7% to 164,067 vehicles. Its stock rose ~5.0% in the week ending September 1.

L Brands (LB) released its August 2017 sales report on August 31. Its net sales for the four weeks ending August 26, 2017, fell 1.0% YoY to $842.1 million. Its comparable sales also fell 4.0% in August. The company’s exit from the swim and apparel categories impacted comparable store sales for Victoria’s Secret by three percentage points and overall sales by two percentage points. As of September 1, the stock rose 2.4% last week.

[“Source-Market Realist”]