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Mensch und Marke am Scheideweg – eine Frage des Vertrauens

Das Ringen um Aufmerksamkeit beim Kunden wird zunehmend härter: online, offline und mobil. Globaler Wettbewerb und zunehmende Vergleichbarkeit sorgen für schwindende Markenloyalität, die Konkurrenz scheint oft nur einen Klick entfernt. Hinzu kommt, dass Konsumenten heute zwischen verschiedenen Geräten wechseln und auf unterschiedlichsten Kanälen mit Marken interagieren. Die klassische Customer Journey wird seit Jahren komplexer, ein ehemals präzises Bild möglicher Interessenten verschwimmt zusehends mit der steigenden Anzahl an Touchpoints, die Unternehmen heute bieten (müssen).

Christoph Kull

Wer in diesen Zeiten nun einfach die eigene „Lautstärke“ erhöht, um noch Gehör zu finden, trägt meiner Meinung nach zum kollektiven Dilemma bei, in dem wir uns befinden. Ein erhöhtes Grundrauschen ist kontraproduktiv, ressourcenintensiv auf Unternehmensseite und für Konsumenten wird es bestenfalls unübersichtlicher, oftmals leider anstrengender. Wirklich zum Kunden durchdringen kann heute nur, wer konsequent auf Relevanz statt auf Marktschreier-Mentalität setzt. Voraussetzung dafür ist jedoch ein präzises Bild der (potentiellen) Kunden, um diese hochpersonalisierte Ansprache und individuelle Relevanz erfolgreich anbieten zu können. Das funktioniert nur über Daten und an dieser Stelle kommt Vertrauen ins Spiel.

Vertrauen ist der Schlüssel jeder erfolgreichen (Kunden-)Beziehung

Was zeichnet eine gute Beziehung zwischen Mensch und Marke aus? Zum einen sicherlich eine gute Erreichbarkeit im Bedarfsfall und eine hohe Konsistenz in Aussage und Markenauftritt über alle Kanäle. Zum anderen aber – und da unterscheidet sich die Kundenbeziehung nicht sonderlich von der zwischenmenschlichen – entscheidet das Vertrauen über die Qualität der jeweiligen Beziehung. Wer die Datenschutzgrundverordnung (DSGVO), die jüngste Rechtsprechung des EuGH zur Opt-in Pflicht für Trackingtools oder den fortschreitenden Standard populärer Browser, Drittanbieter-Cookies automatisch zu blocken nun verantwortlich für schwindenden Kundenkontakt macht, sollte sich fragen, welche Art von Beziehung das eigene Unternehmen bislang zu seinen Kunden unterhält. Ich bin überzeugt, dass wir an einem Punkt angekommen sind, an dem Marken das Verhältnis zu ihren Kunden überdenken und neu definieren sollten.

Für echte Beziehungen gibt es keinen Shortcut: Vertrauen wird langsam aufgebaut und ist ein hohes Gut, mit dem wir behutsam umgehen müssen. In keinem Fall erscheint es mehr ratsam, dies in die Hände Dritter zu legen. Anders gesagt: Marken sollten es zu einer ihrer ureigenen Kompetenzen machen, das Vertrauen ihrer Kunden zu gewinnen und zu pflegen. Dafür sind Transparenz und Wahlfreiheit gefragt: In einer neuen Beziehung lernen wir unseren Partner schließlich auch Schritt für Schritt besser kennen. Manches erzählt man gleich beim ersten Treffen, persönlichere Details geben wir erst preis, wenn wir dem Anderen vertrauen. Gleiches gilt auch für die Beziehung zwischen Marke und Kunde: Vertrauen und ein klar ersichtlicher Mehrwert, dass auf Basis von besseren Daten auch das Kundenerlebnis viel besser wird, sind entscheidend, um eine echte, langfristige Beziehung zu etablieren und authentisch mit den eigenen Kunden kommunizieren zu können. Der Weg zu diesem neuen Verhältnis führt meiner Meinung nach zwangsläufig über die eigene Datenwirtschaft. First Party Data ist der ehrliche unverstellte Blick auf die Qualität meiner Kundenbeziehungen. Das zeigt auch unsere im vergangenen Herbst veröffentlichte Studie „Across the Ages“: Für ein besseres Kundenerlebnis würde die Mehrheit der Konsumenten ihre Daten durchaus mit einer Marke teilen – sofern sie dieser vertrauen.

Je größer das Vertrauen im Laufe der Beziehung wird, desto eher sind Kunden bereit, weitere Daten mit einer Marke zu teilen – diese können sich mit dem Einlösen Ihres Versprechens von passgenaueren Informationen und Angeboten revanchieren. Das wiederum steigert die Kundenzufriedenheit und damit auch ihre Loyalität. Wer in diese Form von Beziehung investiert, hebt sich von der Konkurrenz ab und investiert in die eigene Wettbewerbsfähigkeit.

Autor: Christoph Kull

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How To Become a Data-Driven Organization

1. Which capabilities do organizations need to become data-driven organizations?

The data-driven organization is not a new concept. Put simply, any business that is making business decisions based on facts, rather than based on gut feelings, opinions, and emotions, is a data-driven company. In a data-driven organization not only senior management makes data-driven decisions, but all decisions at all levels are made based on facts. It is therefore about strategic decisions: “are we extending our services to another industry?”, Tactical decisions: “are we hiring this applicant?”, and operational decisions in the workplace: “are we giving this customer a discount?”

Data-driven organizations make sound decisions in a continuous data-driven business cycle. This cycle requires the following three capabilities:

  1. Tech-savvy (Data creation & integration): Ability to create and collect all relevant digital data, and integrate and structure this data into information.
  2. Data fluency (BI & Analytics): Ability to deduce intelligence & insights from data & information.
  3. Data literacy (Decision management): Ability to make decisions & formulate actions based upon intelligence & insights.

HotItem_Data_Driven_Organization

Figure 1: data-driven business cycle with the required capabilities.

Most organizations face difficulties in meeting the technical and organizational requirements to become a data-driven entity. Gartner forecasted that 80% of companies would address their lack of proficiency in data literacy by 2020. Many organizations now recognize this and are starting to change their perspective towards data and analytics. They are beginning to understand that data and analytics can be a significant factor in creating value and shaping business strategies for data-driven businesses. One example is H&M Group’s data mesh journey, a domain-based approach to setting up data architecture within the company.

Tech-savvy capability

Without a big data & analytics platform, the organization is literally driving with blindfolds. Yet qualified people with expertise on the cloud, big data, and data science are scarce and hard to get. And it’s even harder to keep them because a high salary and job security are not enough to keep them satisfied. And even if they are staying with you, they need constant adaptation and learning.

Data fluency capability

Like being fluent in a language, data fluency enables people to express ideas about data in a shared language. In a business context, data fluency connects employees across roles through a set of standards, processes, tools, and terms. Data fluent employees can turn piles of big data into actionable insights because they understand how to interpret it, know the data that is and isn’t available, as well as how to use it appropriately.

Data literacy capability

Data literacy is the ability to read, work with, analyze, and argue with data. Much like literacy as a general concept, data literacy focuses on the competencies involved in working with data.

Every employee on all levels needs technical skills. But being tech-savvy is not enough, soft skills are far more important. Two kinds of soft skills, in particular, are essential:

  1. critical thinking skills: agility, collaboration, creativity, and problem-solving
  2. business skills: communication, negotiation, leadership, project management, planning, delegation, time management, privacy, and ethics.

But the most crucial success factor is the right mindset: Have an open-minded growth mindset (instead of a fixed mindset). Every employee must be accountable for his own success and learning journey. By far the biggest challenge and learning curve is for senior management. Data-driven businesses increase transparency, and transparency reduces power. If that isn’t threatening enough, the rise of Artificial intelligent driven automated decision making is potentially degrading managers from drivers behind the wheel to guiding passengers.

2. Strategic roadmap towards a data-driven enterprise

All three capabilities must be developed and maintained guided by an overarching strategic roadmap towards a data-driven culture. Building a data-driven enterprise is not just about encouraging the use of data in decision-making. Data and analytics leaders must lead the development of the correct competencies and rebalance work to be consistent with their enterprise’s ambitions for generating information value.

A common mistake that organizations make trying to develop a data analytics capability is to hire brilliant data engineers and scientists, let them experiment, and hope for the best. This will surely not lead to analytic solutions that are embedded in the organization and deliver sustainable business value. Don’t treat data and analytics as supportive and secondary to your business initiatives.

First, develop an Enterprise architecture, and let that be the blueprint for further development of the existing data analytics platform. This approach will ensure that the business strategy is aligned with the technical capabilities and actionable insights lead to actions that improve strategic objectives.

Digital transformation is a human transformation: it is not a technological program but a strategic roadmap towards a data-driven culture. Therefore you’ll need an Integral Data-Driven educational and onboarding program’ that is measurable, personalized, affordable and rapidly scalable. Bear in mind that talent is always the constraining factor. There are three crucial factors for every person to make a successful data-driven learning journey:

1) Ambition: The desire and will to change
2) Ability: the skills and knowledge to learn
3) Allowed: the perception that change is supported and permitted

Figure 2: Strategic roadmap towards a data-driven culture

The Strategic roadmap towards a data-driven enterprise consists of two phases:

  1. ROADMAP PHASE
  2. CHANGE PROGRAM PHASE

ROADMAP PHASE

Start with an organizational assessment that analyses the drivers and impacts of the transformation on the organization, assesses the preparedness of the organizational entities to adopt the transformation, and assess the “people and organizational” risks associated with the transformation. Align the business strategy with an integrated data-driven transformation strategy.

CHANGE PROGRAM PHASE

The change program consists of five iterative steps:

  1. CHANGE PLAN
  2. AWARENESS
  3. EDUCATION
  4. LEARNING & EMBEDDING INTO ORGANISATION
  5. PEOPLE ANALYTICS & TRANSITION MONITORING

CHANGE PROGRAM

Develop an integral change program that is optimally tailored to the employee’s level of knowledge and business situation. Use the concept of ‘Education as a Service (EAAS)’ as a framework. Customize and personalize training courses, where possible and needed. Sometimes online learning works best, in other cases team learning is more effective.

AWARENESS

Creating awareness through storytelling and learning journeys. Active commitment and communication of higher management is a key success factor.

LEARNING & EMBEDDING IN ORGANISATIONAL CULTURE

Cultural reinforcement is created by training on the job, apply what’s learned in practice and a continuous feedback loop. Coaching should focus on the three personal success factors: Ambition, Ability and Allowed.

PEOPLE ANALYTICS & TRANSITION MONITORING

Control the learning transition by making the transition data-driven. Develop a BI & Analytics system to monitor the personal learning journey of every employee, as well as monitoring the crucial transition drivers.

CONCLUSIONS

The journey to become and thrive as a data-driven organization is a data-driven human transformation. This transformation is linked with business vision and strategy. Manage the cultural transition with an integral data-driven educational & onboarding program. Monitor the learning journey with people analytics. Focus on the sustainable learning of technical as well as soft skills. Allow room for experiments. Start today. Learning is fun!

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Mark Gallagher: Driving The Future Towards High-Performance Through Big Data

Mark_Gallagher_Driving_The_Future_Big_Data

The future of data-driven organizations has arrived and spearheading businesses towards operational excellence is the vision that Mark Gallagher, the founder and CEO of Performance Insights and Industry Analyst at Formula One, continues to advocate.

As organizations start to adopt more data-driven strategies, Gallagher shares with us the challenges and solutions in which Big Data presents, the opportunities in which disruptive technologies can provide in tandem with data and analytics, and the future it holds for businesses and beyond.

Mark_Gallagher_Driving_High_Performance_Through_Big_Data

The Challenges and Solutions of Data-Driven High Performance

Big Data and analytics have quickly become the key ingredient that businesses need to integrate to remain as a high-performing and agile organization in today’s modern industry. Nevertheless, there are challenges that businesses have to overcome before being able to transform into a data-driven organization.

One such challenge that Gallagher notes is the need for organizations to understand and find which data is most relevant to unlocking new opportunities and not rely on established systems.

“We may wish to gather data from the areas where we have some understanding,” notes Gallagher. “However, the real opportunity comes from questioning established systems and processes and examing data around the unknowns.”

While finding and utilizing data effectively is still a major challenge for most organizations, Gallagher believes the solution lies in organizations finding the right partners and using the right emerging technology to help improve performances.

“It is vital to work with the right partners to develop systems that can make rapid use of data”, Gallagher points out. “Real-time data encourages and facilitates real-time decision making, and this is where the power of AI kicks in.”

In the world of Formula One, Gallagher found that both the quality and speed of decision-making have improved dramatically with the help of partners and AI to help understand and utilize data. This enabled Gallagher to guarantee much higher levels of quality, reliability, risk management and performance, allowing them to “avoid negative outcomes and guarantee more positive ones.”

Utilizing The Power of Disruptive Technology

On its own, Big Data has proven to be a disruptive technology. However, Gallagher believes that several emerging technologies can be “game-changers” for the traditional business processes.

“The opportunities afforded by AI and Blockchain technology are only just being realized, and far from dehumanizing businesses”. Gallagher continues, “these tools will enable more people and organizations to work together seamlessly to drive improved outcomes for their customers, businesses and supply chains.”

The benefits of emerging technologies go beyond organizational efficiency and Gallagher points out how Internet of Things (IoT) and artificial intelligence have helped build a more connected and data-driven environment in Formula One.

“We operate a fully connected environment so that we can manage our assets remotely, monitoring performance, quality, gathering diagnostic information and ultimate managing the product life cycle better than ever”, Gallagher remarked on the usage of IoT and artificial intelligence platforms.

Gallagher sees the innovation that IoT and artificial intelligence brings to Formula One, providing information to make better use of their resources and dramatically improve their manufacturing systems. “In creating a digital twin of our product, we have moved to an environment where we can manufacture and manage much more efficiently.”

The Big Future of Big Data and Analytics

Focusing on the data that matters should be the priority for organizations, and as vast amounts of data become increasingly available, Gallagher and Formula One needs to work with solutions that cut to the core of the issues and opportunities that are affecting businesses.

Gallagher points out how Big Data and analytics can be utilized in new ways for businesses and society as a whole in the future, noting that in “a data-rich world, we can mine more opportunities to add value.”

Beyond the profit margins, Big Data has the opportunity to develop innovative solutions and Gallagher shares this enthusiasm saying that he is ”very optimistic that many of the problems facing the world today will find their solutions in technology that develops as the result of having the data to understand issues properly.”

At the end of the day, data is just information and when businesses can access a better quality of information, they can expect to improve outcomes across all areas of operations.